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Federated Submodel Optimization for Hot and Cold Data Features | https://papers.nips.cc/paper_files/paper/2022/hash/002262941c9edfd472a79298b2ac5e17-Abstract-Conference.html | Yucheng Ding, Chaoyue Niu, Fan Wu, Shaojie Tang, Chengfei Lyu, yanghe feng, Guihai Chen | https://papers.nips.cc/paper_files/paper/2022/hash/002262941c9edfd472a79298b2ac5e17-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17527-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/002262941c9edfd472a79298b2ac5e17-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/002262941c9edfd472a79298b2ac5e17-Supplemental-Conference.pdf | We focus on federated learning in practical recommender systems and natural language processing scenarios. The global model for federated optimization typically contains a large and sparse embedding layer, while each client’s local data tend to interact with part of features, updating only a small submodel with the feature-related embedding vectors. We identify a new and important issue that distinct data features normally involve different numbers of clients, generating the differentiation of hot and cold features. We further reveal that the classical federated averaging algorithm (FedAvg) or its variants, which randomly selects clients to participate and uniformly averages their submodel updates, will be severely slowed down, because different parameters of the global model are optimized at different speeds. More specifically, the model parameters related to hot (resp., cold) features will be updated quickly (resp., slowly). We thus propose federated submodel averaging (FedSubAvg), which introduces the number of feature-related clients as the metric of feature heat to correct the aggregation of submodel updates. We prove that due to the dispersion of feature heat, the global objective is ill-conditioned, and FedSubAvg works as a suitable diagonal preconditioner. We also rigorously analyze FedSubAvg’s convergence rate to stationary points. We finally evaluate FedSubAvg over several public and industrial datasets. The evaluation results demonstrate that FedSubAvg significantly outperforms FedAvg and its variants. | null | null |
On Kernelized Multi-Armed Bandits with Constraints | https://papers.nips.cc/paper_files/paper/2022/hash/00295cede6e1600d344b5cd6d9fd4640-Abstract-Conference.html | Xingyu Zhou, Bo Ji | https://papers.nips.cc/paper_files/paper/2022/hash/00295cede6e1600d344b5cd6d9fd4640-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18113-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/00295cede6e1600d344b5cd6d9fd4640-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/00295cede6e1600d344b5cd6d9fd4640-Supplemental-Conference.pdf | We study a stochastic bandit problem with a general unknown reward function and a general unknown constraint function. Both functions can be non-linear (even non-convex) and are assumed to lie in a reproducing kernel Hilbert space (RKHS) with a bounded norm. This kernelized bandit setup strictly generalizes standard multi-armed bandits and linear bandits. In contrast to safety-type hard constraints studied in prior works, we consider soft constraints that may be violated in any round as long as the cumulative violations are small, which is motivated by various practical applications. Our ultimate goal is to study how to utilize the nature of soft constraints to attain a finer complexity-regret-constraint trade-off in the kernelized bandit setting. To this end, leveraging primal-dual optimization, we propose a general framework for both algorithm design and performance analysis. This framework builds upon a novel sufficient condition, which not only is satisfied under general exploration strategies, including \emph{upper confidence bound} (UCB), \emph{Thompson sampling} (TS), and new ones based on \emph{random exploration}, but also enables a unified analysis for showing both sublinear regret and sublinear or even zero constraint violation. We demonstrate the superior performance of our proposed algorithms via numerical experiments based on both synthetic and real-world datasets. Along the way, we also make the first detailed comparison between two popular methods for analyzing constrained bandits and Markov decision processes (MDPs) by discussing the key difference and some subtleties in the analysis, which could be of independent interest to the communities. | null | null |
Geometric Order Learning for Rank Estimation | https://papers.nips.cc/paper_files/paper/2022/hash/00358de35a101a372ea0412bed913c86-Abstract-Conference.html | Seon-Ho Lee, Nyeong Ho Shin, Chang-Su Kim | https://papers.nips.cc/paper_files/paper/2022/hash/00358de35a101a372ea0412bed913c86-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17861-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/00358de35a101a372ea0412bed913c86-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/00358de35a101a372ea0412bed913c86-Supplemental-Conference.zip | A novel approach to rank estimation, called geometric order learning (GOL), is proposed in this paper. First, we construct an embedding space, in which the direction and distance between objects represent order and metric relations between their ranks, by enforcing two geometric constraints: the order constraint compels objects to be sorted according to their ranks, while the metric constraint makes the distance between objects reflect their rank difference. Then, we perform the simple $k$ nearest neighbor ($k$-NN) search in the embedding space to estimate the rank of a test object. Moreover, to assess the quality of embedding spaces for rank estimation, we propose a metric called discriminative ratio for ranking (DRR). Extensive experiments on facial age estimation, historical color image (HCI) classification, and aesthetic score regression demonstrate that GOL constructs effective embedding spaces and thus yields excellent rank estimation performances. The source codes are available at https://github.com/seon92/GOL | null | null |
Structured Recognition for Generative Models with Explaining Away | https://papers.nips.cc/paper_files/paper/2022/hash/003a96110b7134d678cb675c6aea6c7d-Abstract-Conference.html | Changmin Yu, Hugo Soulat, Neil Burgess, Maneesh Sahani | https://papers.nips.cc/paper_files/paper/2022/hash/003a96110b7134d678cb675c6aea6c7d-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17779-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/003a96110b7134d678cb675c6aea6c7d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/003a96110b7134d678cb675c6aea6c7d-Supplemental-Conference.zip | A key goal of unsupervised learning is to go beyond density estimation and sample generation to reveal the structure inherent within observed data. Such structure can be expressed in the pattern of interactions between explanatory latent variables captured through a probabilistic graphical model. Although the learning of structured graphical models has a long history, much recent work in unsupervised modelling has instead emphasised flexible deep-network-based generation, either transforming independent latent generators to model complex data or assuming that distinct observed variables are derived from different latent nodes. Here, we extend amortised variational inference to incorporate structured factors over multiple variables, able to capture the observation-induced posterior dependence between latents that results from “explaining away” and thus allow complex observations to depend on multiple nodes of a structured graph. We show that appropriately parametrised factors can be combined efficiently with variational message passing in rich graphical structures. We instantiate the framework in nonlinear Gaussian Process Factor Analysis, evaluating the structured recognition framework using synthetic data from known generative processes. We fit the GPFA model to high-dimensional neural spike data from the hippocampus of freely moving rodents, where the model successfully identifies latent signals that correlate with behavioural covariates. | null | null |
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement | https://papers.nips.cc/paper_files/paper/2022/hash/005413e90d003d13886019607b037f52-Abstract-Conference.html | Cian Naik, Judith Rousseau, Trevor Campbell | https://papers.nips.cc/paper_files/paper/2022/hash/005413e90d003d13886019607b037f52-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19127-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/005413e90d003d13886019607b037f52-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/005413e90d003d13886019607b037f52-Supplemental-Conference.zip | Bayesian coresets approximate a posterior distribution by building a small weighted subset of the data points. Any inference procedure that is too computationally expensive to be run on the full posterior can instead be run inexpensively on the coreset, with results that approximate those on the full data. However, current approaches are limited by either a significant run-time or the need for the user to specify a low-cost approximation to the full posterior. We propose a Bayesian coreset construction algorithm that first selects a uniformly random subset of data, and then optimizes the weights using a novel quasi-Newton method. Our algorithm is a simple to implement, black-box method, that does not require the user to specify a low-cost posterior approximation. It is the first to come with a general high-probability bound on the KL divergence of the output coreset posterior. Experiments demonstrate that our method provides significant improvements in coreset quality against alternatives with comparable construction times, with far less storage cost and user input required. | null | null |
What You See is What You Classify: Black Box Attributions | https://papers.nips.cc/paper_files/paper/2022/hash/0073cc73e1873b35345209b50a3dab66-Abstract-Conference.html | Steven Stalder, Nathanael Perraudin, Radhakrishna Achanta, Fernando Perez-Cruz, Michele Volpi | https://papers.nips.cc/paper_files/paper/2022/hash/0073cc73e1873b35345209b50a3dab66-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19444-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0073cc73e1873b35345209b50a3dab66-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0073cc73e1873b35345209b50a3dab66-Supplemental-Conference.zip | An important step towards explaining deep image classifiers lies in the identification of image regions that contribute to individual class scores in the model's output. However, doing this accurately is a difficult task due to the black-box nature of such networks. Most existing approaches find such attributions either using activations and gradients or by repeatedly perturbing the input. We instead address this challenge by training a second deep network, the Explainer, to predict attributions for a pre-trained black-box classifier, the Explanandum. These attributions are provided in the form of masks that only show the classifier-relevant parts of an image, masking out the rest. Our approach produces sharper and more boundary-precise masks when compared to the saliency maps generated by other methods. Moreover, unlike most existing approaches, ours is capable of directly generating very distinct class-specific masks in a single forward pass. This makes the proposed method very efficient during inference. We show that our attributions are superior to established methods both visually and quantitatively with respect to the PASCAL VOC-2007 and Microsoft COCO-2014 datasets. | null | null |
Adaptive Interest for Emphatic Reinforcement Learning | https://papers.nips.cc/paper_files/paper/2022/hash/008079ec00eec9760ee93af5434ee932-Abstract-Conference.html | Martin Klissarov, Rasool Fakoor, Jonas W. Mueller, Kavosh Asadi, Taesup Kim, Alexander J. Smola | https://papers.nips.cc/paper_files/paper/2022/hash/008079ec00eec9760ee93af5434ee932-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18361-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/008079ec00eec9760ee93af5434ee932-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/008079ec00eec9760ee93af5434ee932-Supplemental-Conference.pdf | Emphatic algorithms have shown great promise in stabilizing and improving reinforcement learning by selectively emphasizing the update rule. Although the emphasis fundamentally depends on an interest function which defines the intrinsic importance of each state, most approaches simply adopt a uniform interest over all states (except where a hand-designed interest is possible based on domain knowledge). In this paper, we investigate adaptive methods that allow the interest function to dynamically vary over states and iterations. In particular, we leverage meta-gradients to automatically discover online an interest function that would accelerate the agent’s learning process. Empirical evaluations on a wide range of environments show that adapting the interest is key to provide significant gains. Qualitative analysis indicates that the learned interest function emphasizes states of particular importance, such as bottlenecks, which can be especially useful in a transfer learning setting. | null | null |
Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning | https://papers.nips.cc/paper_files/paper/2022/hash/00bb4e415ef117f2dee2fc3b778d806d-Abstract-Conference.html | Dongze Lian, Daquan Zhou, Jiashi Feng, Xinchao Wang | https://papers.nips.cc/paper_files/paper/2022/hash/00bb4e415ef117f2dee2fc3b778d806d-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18656-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/00bb4e415ef117f2dee2fc3b778d806d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/00bb4e415ef117f2dee2fc3b778d806d-Supplemental-Conference.pdf | Existing fine-tuning methods either tune all parameters of the pre-trained model (full fine-tuning), which is not efficient, or only tune the last linear layer (linear probing), which suffers a significant accuracy drop compared to the full fine-tuning. In this paper, we propose a new parameter-efficient fine-tuning method termed as SSF, representing that researchers only need to Scale and Shift the deep Features extracted by a pre-trained model to catch up with the performance of full fine-tuning. In this way, SSF also surprisingly outperforms other parameter-efficient fine-tuning approaches even with a smaller number of tunable parameters. Furthermore, different from some existing parameter-efficient fine-tuning methods (e.g., Adapter or VPT) that introduce the extra parameters and computational cost in the training and inference stages, SSF only adds learnable parameters during the training stage, and these additional parameters can be merged into the original pre-trained model weights via re-parameterization in the inference phase. With the proposed SSF, our model obtains 2.46% (90.72% vs. 88.54%) and 11.48% (73.10% vs. 65.57%) performance improvement on FGVC and VTAB-1k in terms of Top-1 accuracy compared to the full fine-tuning but only fine-tuning about 0.3M parameters. We also conduct amounts of experiments in various model families (CNNs, Transformers, and MLPs) and datasets. Results on 26 image classification datasets in total and 3 robustness & out-of-distribution datasets show the effectiveness of SSF. Code is available at https://github.com/dongzelian/SSF. | null | null |
Zero-Shot Video Question Answering via Frozen Bidirectional Language Models | https://papers.nips.cc/paper_files/paper/2022/hash/00d1f03b87a401b1c7957e0cc785d0bc-Abstract-Conference.html | Antoine Yang, Antoine Miech, Josef Sivic, Ivan Laptev, Cordelia Schmid | https://papers.nips.cc/paper_files/paper/2022/hash/00d1f03b87a401b1c7957e0cc785d0bc-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17847-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/00d1f03b87a401b1c7957e0cc785d0bc-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/00d1f03b87a401b1c7957e0cc785d0bc-Supplemental-Conference.zip | Video question answering (VideoQA) is a complex task that requires diverse multi-modal data for training. Manual annotation of question and answers for videos, however, is tedious and prohibits scalability. To tackle this problem, recent methods consider zero-shot settings with no manual annotation of visual question-answer. In particular, a promising approach adapts frozen autoregressive language models pretrained on Web-scale text-only data to multi-modal inputs. In contrast, we here build on frozen bidirectional language models (BiLM) and show that such an approach provides a stronger and cheaper alternative for zero-shot VideoQA. In particular, (i) we combine visual inputs with the frozen BiLM using light trainable modules, (ii) we train such modules using Web-scraped multi-modal data, and finally (iii) we perform zero-shot VideoQA inference through masked language modeling, where the masked text is the answer to a given question. Our proposed approach, FrozenBiLM, outperforms the state of the art in zero-shot VideoQA by a significant margin on a variety of datasets, including LSMDC-FiB, iVQA, MSRVTT-QA, MSVD-QA, ActivityNet-QA, TGIF-FrameQA, How2QA and TVQA. It also demonstrates competitive performance in the few-shot and fully-supervised setting. Our code and models are publicly available at https://github.com/antoyang/FrozenBiLM. | null | null |
Active Learning with Neural Networks: Insights from Nonparametric Statistics | https://papers.nips.cc/paper_files/paper/2022/hash/01025a4e79355bb37a10ba39605944b5-Abstract-Conference.html | Yinglun Zhu, Robert Nowak | https://papers.nips.cc/paper_files/paper/2022/hash/01025a4e79355bb37a10ba39605944b5-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17239-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/01025a4e79355bb37a10ba39605944b5-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/01025a4e79355bb37a10ba39605944b5-Supplemental-Conference.pdf | Deep neural networks have great representation power, but typically require large numbers of training examples. This motivates deep active learning methods that can significantly reduce the amount of labeled training data. Empirical successes of deep active learning have been recently reported in the literature, however, rigorous label complexity guarantees of deep active learning have remained elusive. This constitutes a significant gap between theory and practice. This paper tackles this gap by providing the first near-optimal label complexity guarantees for deep active learning. The key insight is to study deep active learning from the nonparametric classification perspective. Under standard low noise conditions, we show that active learning with neural networks can provably achieve the minimax label complexity, up to disagreement coefficient and other logarithmic terms. When equipped with an abstention option, we further develop an efficient deep active learning algorithm that achieves $\mathsf{polylog}(\frac{1}{\varepsilon})$ label complexity, without any low noise assumptions. We also provide extensions of our results beyond the commonly studied Sobolev/H\"older spaces and develop label complexity guarantees for learning in Radon $\mathsf{BV}^2$ spaces, which have recently been proposed as natural function spaces associated with neural networks. | null | null |
IM-Loss: Information Maximization Loss for Spiking Neural Networks | https://papers.nips.cc/paper_files/paper/2022/hash/010c5ba0cafc743fece8be02e7adb8dd-Abstract-Conference.html | Yufei Guo, Yuanpei Chen, Liwen Zhang, Xiaode Liu, Yinglei Wang, Xuhui Huang, Zhe Ma | https://papers.nips.cc/paper_files/paper/2022/hash/010c5ba0cafc743fece8be02e7adb8dd-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17524-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/010c5ba0cafc743fece8be02e7adb8dd-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/010c5ba0cafc743fece8be02e7adb8dd-Supplemental-Conference.pdf | Spiking Neural Network (SNN), recognized as a type of biologically plausible architecture, has recently drawn much research attention. It transmits information by $0/1$ spikes. This bio-mimetic mechanism of SNN demonstrates extreme energy efficiency since it avoids any multiplications on neuromorphic hardware. However, the forward-passing $0/1$ spike quantization will cause information loss and accuracy degradation. To deal with this problem, the Information maximization loss (IM-Loss) that aims at maximizing the information flow in the SNN is proposed in the paper. The IM-Loss not only enhances the information expressiveness of an SNN directly but also plays a part of the role of normalization without introducing any additional operations (\textit{e.g.}, bias and scaling) in the inference phase. Additionally, we introduce a novel differentiable spike activity estimation, Evolutionary Surrogate Gradients (ESG) in SNNs. By appointing automatic evolvable surrogate gradients for spike activity function, ESG can ensure sufficient model updates at the beginning and accurate gradients at the end of the training, resulting in both easy convergence and high task performance. Experimental results on both popular non-spiking static and neuromorphic datasets show that the SNN models trained by our method outperform the current state-of-the-art algorithms. | null | null |
Using natural language and program abstractions to instill human inductive biases in machines | https://papers.nips.cc/paper_files/paper/2022/hash/0113ef4642264adc2e6924a3cbbdf532-Abstract-Conference.html | Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta, Raja Marjieh, Michael Y Hu, Robert Hawkins, Jonathan D Cohen, nathaniel daw, Karthik Narasimhan, Tom Griffiths | https://papers.nips.cc/paper_files/paper/2022/hash/0113ef4642264adc2e6924a3cbbdf532-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17141-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0113ef4642264adc2e6924a3cbbdf532-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0113ef4642264adc2e6924a3cbbdf532-Supplemental-Conference.zip | Strong inductive biases give humans the ability to quickly learn to perform a variety of tasks. Although meta-learning is a method to endow neural networks with useful inductive biases, agents trained by meta-learning may sometimes acquire very different strategies from humans. We show that co-training these agents on predicting representations from natural language task descriptions and programs induced to generate such tasks guides them toward more human-like inductive biases. Human-generated language descriptions and program induction models that add new learned primitives both contain abstract concepts that can compress description length. Co-training on these representations result in more human-like behavior in downstream meta-reinforcement learning agents than less abstract controls (synthetic language descriptions, program induction without learned primitives), suggesting that the abstraction supported by these representations is key. | null | null |
Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits | https://papers.nips.cc/paper_files/paper/2022/hash/01c4593d60a020fed5607944330106b1-Abstract-Conference.html | Ruibo Liu, Chenyan Jia, Ge Zhang, Ziyu Zhuang, Tony Liu, Soroush Vosoughi | https://papers.nips.cc/paper_files/paper/2022/hash/01c4593d60a020fed5607944330106b1-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16643-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/01c4593d60a020fed5607944330106b1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/01c4593d60a020fed5607944330106b1-Supplemental-Conference.pdf | We present Second Thoughts, a new learning paradigm that enables language models (LMs) to re-align with human values. By modeling the chain-of-edits between value-unaligned and value-aligned text, with LM fine-tuning and additional refinement through reinforcement learning, Second Thoughts not only achieves superior performance in three value alignment benchmark datasets but also shows strong human-value transfer learning ability in few-shot scenarios. The generated editing steps also offer better interpretability and ease for interactive error correction. Extensive human evaluations further confirm its effectiveness. | null | null |
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery | https://papers.nips.cc/paper_files/paper/2022/hash/01c561df365429f33fcd7a7faa44c985-Abstract-Conference.html | Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David Lobell, Stefano Ermon | https://papers.nips.cc/paper_files/paper/2022/hash/01c561df365429f33fcd7a7faa44c985-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16878-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/01c561df365429f33fcd7a7faa44c985-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/01c561df365429f33fcd7a7faa44c985-Supplemental-Conference.pdf | Unsupervised pre-training methods for large vision models have shown to enhance performance on downstream supervised tasks. Developing similar techniques for satellite imagery presents significant opportunities as unlabelled data is plentiful and the inherent temporal and multi-spectral structure provides avenues to further improve existing pre-training strategies. In this paper, we present SatMAE, a pre-training framework for temporal or multi-spectral satellite imagery based on Masked Autoencoder (MAE). To leverage temporal information, we include a temporal embedding along with independently masking image patches across time. In addition, we demonstrate that encoding multi-spectral data as groups of bands with distinct spectral positional encodings is beneficial. Our approach yields strong improvements over previous state-of-the-art techniques, both in terms of supervised learning performance on benchmark datasets (up to $\uparrow$ 7%), and transfer learning performance on downstream remote sensing tasks, including land cover classification (up to $\uparrow$ 14%) and semantic segmentation. Code and data are available on the project website: https://sustainlab-group.github.io/SatMAE/ | null | null |
On Sample Optimality in Personalized Collaborative and Federated Learning | https://papers.nips.cc/paper_files/paper/2022/hash/01cea7793f3c68af2e4989fc66bf8fb0-Abstract-Conference.html | Mathieu Even, Laurent Massoulié, Kevin Scaman | https://papers.nips.cc/paper_files/paper/2022/hash/01cea7793f3c68af2e4989fc66bf8fb0-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17923-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/01cea7793f3c68af2e4989fc66bf8fb0-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/01cea7793f3c68af2e4989fc66bf8fb0-Supplemental-Conference.pdf | In personalized federated learning, each member of a potentially large set of agents aims to train a model minimizing its loss function averaged over its local data distribution. We study this problem under the lens of stochastic optimization, focusing on a scenario with a large number of agents, that each possess very few data samples from their local data distribution. Specifically, we prove novel matching lower and upper bounds on the number of samples required from all agents to approximately minimize the generalization error of a fixed agent. We provide strategies matching these lower bounds, based on a gradient filtering approach: given prior knowledge on some notion of distance between local data distributions, agents filter and aggregate stochastic gradients received from other agents, in order to achieve an optimal bias-variance trade-off. Finally, we quantify the impact of using rough estimations of the distances between local distributions of agents, based on a very small number of local samples. | null | null |
Offline Multi-Agent Reinforcement Learning with Knowledge Distillation | https://papers.nips.cc/paper_files/paper/2022/hash/01d78b294d80491fecddea897cf03642-Abstract-Conference.html | Wei-Cheng Tseng, Tsun-Hsuan Johnson Wang, Yen-Chen Lin, Phillip Isola | https://papers.nips.cc/paper_files/paper/2022/hash/01d78b294d80491fecddea897cf03642-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17221-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/01d78b294d80491fecddea897cf03642-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/01d78b294d80491fecddea897cf03642-Supplemental-Conference.pdf | We introduce an offline multi-agent reinforcement learning ( offline MARL) framework that utilizes previously collected data without additional online data collection. Our method reformulates offline MARL as a sequence modeling problem and thus builds on top of the simplicity and scalability of the Transformer architecture. In the fashion of centralized training and decentralized execution, we propose to first train a teacher policy as if the MARL dataset is generated by a single agent. After the teacher policy has identified and recombined the "good" behavior in the dataset, we create separate student policies and distill not only the teacher policy's features but also its structural relations among different agents' features to student policies. Despite its simplicity, the proposed method outperforms state-of-the-art model-free offline MARL baselines while being more robust to demonstration's quality on several environments. | null | null |
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks | https://papers.nips.cc/paper_files/paper/2022/hash/01db36a646c07c64dd39a92b4eceb417-Abstract-Conference.html | Shuoguang Yang, Xuezhou Zhang, Mengdi Wang | https://papers.nips.cc/paper_files/paper/2022/hash/01db36a646c07c64dd39a92b4eceb417-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18262-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/01db36a646c07c64dd39a92b4eceb417-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/01db36a646c07c64dd39a92b4eceb417-Supplemental-Conference.pdf | Bilevel optimization have gained growing interests, with numerous applications found in meta learning, minimax games, reinforcement learning, and nested composition optimization. This paper studies the problem of decentralized distributed bilevel optimization over a network where agents can only communicate with neighbors, and gives examples from multi-task, multi-agent learning and federated learning.In this paper, we propose a gossip-based distributed bilevel learning algorithm that allows networked agents to solve both the inner and outer optimization problems in a single timescale and share information through network propagation. We show that our algorithm enjoys the $\mathcal{O}(\frac{1}{K \epsilon^2})$ per-agent sample complexity for general nonconvex bilevel optimization and $\mathcal{O}(\frac{1}{K \epsilon})$ for Polyak-Łojasiewicz objective, achieving a speedup that scales linearly with the network size $K$. The sample complexities are optimal in both $\epsilon$ and $K$.We test our algorithm on the examples of hyperparameter tuning and decentralized reinforcement learning. Simulated experiments confirmed that our algorithm achieves the state-of-the-art training efficiency and test accuracy. | null | null |
Conditional Meta-Learning of Linear Representations | https://papers.nips.cc/paper_files/paper/2022/hash/01ecd39ca49ddecc5729ca996304781b-Abstract-Conference.html | Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto | https://papers.nips.cc/paper_files/paper/2022/hash/01ecd39ca49ddecc5729ca996304781b-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19290-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/01ecd39ca49ddecc5729ca996304781b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/01ecd39ca49ddecc5729ca996304781b-Supplemental-Conference.zip | Standard meta-learning for representation learning aims to find a common representation to be shared across multiple tasks. The effectiveness of these methods is often limited when the nuances of the tasks’ distribution cannot be captured by a single representation. In this work we overcome this issue by inferring a conditioning function, mapping the tasks’ side information (such as the tasks’ training dataset itself) into a representation tailored to the task at hand. We study environments in which our conditional strategy outperforms standard meta-learning, such as those in which tasks can be organized in separate clusters according to the representation they share. We then propose a meta-algorithm capable of leveraging this advantage in practice. In the unconditional setting, our method yields a new estimator enjoying faster learning rates and requiring less hyper-parameters to tune than current state-of-the-art methods. Our results are supported by preliminary experiments. | null | null |
Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources | https://papers.nips.cc/paper_files/paper/2022/hash/0206c1c20a18915da23df5e61966fc6a-Abstract-Conference.html | Peter Lippmann, Enrique Fita Sanmartín, Fred A. Hamprecht | https://papers.nips.cc/paper_files/paper/2022/hash/0206c1c20a18915da23df5e61966fc6a-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19313-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0206c1c20a18915da23df5e61966fc6a-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0206c1c20a18915da23df5e61966fc6a-Supplemental-Conference.pdf | Branched optimal transport (BOT) is a generalization of optimal transport in which transportation costs along an edge are subadditive. This subadditivity models an increase in transport efficiency when shipping mass along the same route, favoring branched transportation networks. We here study the NP-hard optimization of BOT networks connecting a finite number of sources and sinks in $\mathbb{R}^2$. First, we show how to efficiently find the best geometry of a BOT network for many sources and sinks, given a topology. Second, we argue that a topology with more than three edges meeting at a branching point is never optimal. Third, we show that the results obtained for the Euclidean plane generalize directly to optimal transportation networks on two-dimensional Riemannian manifolds. Finally, we present a simple but effective approximate BOT solver combining geometric optimization with a combinatorial optimization of the network topology. | null | null |
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis | https://papers.nips.cc/paper_files/paper/2022/hash/0207c9ea9faf66c6e892c3fa3c167b75-Abstract-Conference.html | Shichong Peng, Seyed Alireza Moazenipourasil, Ke Li | https://papers.nips.cc/paper_files/paper/2022/hash/0207c9ea9faf66c6e892c3fa3c167b75-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18700-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0207c9ea9faf66c6e892c3fa3c167b75-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0207c9ea9faf66c6e892c3fa3c167b75-Supplemental-Conference.zip | A persistent challenge in conditional image synthesis has been to generate diverse output images from the same input image despite only one output image being observed per input image. GAN-based methods are prone to mode collapse, which leads to low diversity. To get around this, we leverage Implicit Maximum Likelihood Estimation (IMLE) which can overcome mode collapse fundamentally. IMLE uses the same generator as GANs but trains it with a different, non-adversarial objective which ensures each observed image has a generated sample nearby. Unfortunately, to generate high-fidelity images, prior IMLE-based methods require a large number of samples, which is expensive. In this paper, we propose a new method to get around this limitation, which we dub Conditional Hierarchical IMLE (CHIMLE), which can generate high-fidelity images without requiring many samples. We show CHIMLE significantly outperforms the prior best IMLE, GAN and diffusion-based methods in terms of image fidelity and mode coverage across four tasks, namely night-to-day, 16x single image super-resolution, image colourization and image decompression. Quantitatively, our method improves Fréchet Inception Distance (FID) by 36.9% on average compared to the prior best IMLE-based method, and by 27.5% on average compared to the best non-IMLE-based general-purpose methods. More results and code are available on the project website at https://niopeng.github.io/CHIMLE/. | null | null |
Active Ranking without Strong Stochastic Transitivity | https://papers.nips.cc/paper_files/paper/2022/hash/020e313d40a7c060ed07a10cef287750-Abstract-Conference.html | Hao Lou, Tao Jin, Yue Wu, Pan Xu, Quanquan Gu, Farzad Farnoud | https://papers.nips.cc/paper_files/paper/2022/hash/020e313d40a7c060ed07a10cef287750-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16985-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/020e313d40a7c060ed07a10cef287750-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/020e313d40a7c060ed07a10cef287750-Supplemental-Conference.pdf | Ranking from noisy comparisons is of great practical interest in machine learning. In this paper, we consider the problem of recovering the exact full ranking for a list of items under ranking models that do *not* assume the Strong Stochastic Transitivity property. We propose a $$\delta$$-correct algorithm, Probe-Rank, that actively learns the ranking of the items from noisy pairwise comparisons. We prove a sample complexity upper bound for Probe-Rank, which only depends on the preference probabilities between items that are adjacent in the true ranking. This improves upon existing sample complexity results that depend on the preference probabilities for all pairs of items. Probe-Rank thus outperforms existing methods over a large collection of instances that do not satisfy Strong Stochastic Transitivity. Thorough numerical experiments in various settings are conducted, demonstrating that Probe-Rank is significantly more sample-efficient than the state-of-the-art active ranking method. | null | null |
Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression | https://papers.nips.cc/paper_files/paper/2022/hash/022a39052abf9ca467e268923057dfc0-Abstract-Conference.html | Jason Yecheng Ma, Jason Yan, Dinesh Jayaraman, Osbert Bastani | https://papers.nips.cc/paper_files/paper/2022/hash/022a39052abf9ca467e268923057dfc0-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16796-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/022a39052abf9ca467e268923057dfc0-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/022a39052abf9ca467e268923057dfc0-Supplemental-Conference.pdf | Offline goal-conditioned reinforcement learning (GCRL) promises general-purpose skill learning in the form of reaching diverse goals from purely offline datasets. We propose $\textbf{Go}$al-conditioned $f$-$\textbf{A}$dvantage $\textbf{R}$egression (GoFAR), a novel regression-based offline GCRL algorithm derived from a state-occupancy matching perspective; the key intuition is that the goal-reaching task can be formulated as a state-occupancy matching problem between a dynamics-abiding imitator agent and an expert agent that directly teleports to the goal. In contrast to prior approaches, GoFAR does not require any hindsight relabeling and enjoys uninterleaved optimization for its value and policy networks. These distinct features confer GoFAR with much better offline performance and stability as well as statistical performance guarantee that is unattainable for prior methods. Furthermore, we demonstrate that GoFAR's training objectives can be re-purposed to learn an agent-independent goal-conditioned planner from purely offline source-domain data, which enables zero-shot transfer to new target domains. Through extensive experiments, we validate GoFAR's effectiveness in various problem settings and tasks, significantly outperforming prior state-of-art. Notably, on a real robotic dexterous manipulation task, while no other method makes meaningful progress, GoFAR acquires complex manipulation behavior that successfully accomplishes diverse goals. | null | null |
Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain | https://papers.nips.cc/paper_files/paper/2022/hash/022abe84083d235f7572ca5cba24c51c-Abstract-Conference.html | Yiting Chen, Qibing Ren, Junchi Yan | https://papers.nips.cc/paper_files/paper/2022/hash/022abe84083d235f7572ca5cba24c51c-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19148-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/022abe84083d235f7572ca5cba24c51c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/022abe84083d235f7572ca5cba24c51c-Supplemental-Conference.pdf | The existence of adversarial examples poses concerns for the robustness of convolutional neural networks (CNN), for which a popular hypothesis is about the frequency bias phenomenon: CNNs rely more on high-frequency components (HFC) for classification than humans, which causes the brittleness of CNNs. However, most previous works manually select and roughly divide the image frequency spectrum and conduct qualitative analysis. In this work, we introduce Shapley value, a metric of cooperative game theory, into the frequency domain and propose to quantify the positive (negative) impact of every frequency component of data on CNNs. Based on the Shapley value, we quantify the impact in a fine-grained way and show intriguing instance disparity. Statistically, we investigate adversarial training(AT) and the adversarial attack in the frequency domain. The observations motivate us to perform an in-depth analysis and lead to multiple novel hypotheses about i) the cause of adversarial robustness of the AT model; ii) the fairness problem of AT between different classes in the same dataset; iii) the attack bias on different frequency components. Finally, we propose a Shapley-value guided data augmentation technique for improving the robustness. Experimental results on image classification benchmarks show its effectiveness. | null | null |
Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation | https://papers.nips.cc/paper_files/paper/2022/hash/023d94f44110b9a3c62329beec739772-Abstract-Conference.html | Zhun Zhong, Yuyang Zhao, Gim Hee Lee, Nicu Sebe | https://papers.nips.cc/paper_files/paper/2022/hash/023d94f44110b9a3c62329beec739772-Abstract-Conference.html | NIPS 2022 | null | null | null | null | null | null |
Fully Sparse 3D Object Detection | https://papers.nips.cc/paper_files/paper/2022/hash/0247fa3c511bbc415c8b768ee7b32f9e-Abstract-Conference.html | Lue Fan, Feng Wang, Naiyan Wang, ZHAO-XIANG ZHANG | https://papers.nips.cc/paper_files/paper/2022/hash/0247fa3c511bbc415c8b768ee7b32f9e-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17797-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0247fa3c511bbc415c8b768ee7b32f9e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0247fa3c511bbc415c8b768ee7b32f9e-Supplemental-Conference.zip | As the perception range of LiDAR increases, LiDAR-based 3D object detection becomes a dominant task in the long-range perception task of autonomous driving. The mainstream 3D object detectors usually build dense feature maps in the network backbone and prediction head. However, the computational and spatial costs on the dense feature map are quadratic to the perception range, which makes them hardly scale up to the long-range setting. To enable efficient long-range LiDAR-based object detection, we build a fully sparse 3D object detector (FSD). The computational and spatial cost of FSD is roughly linear to the number of points and independent of the perception range. FSD is built upon the general sparse voxel encoder and a novel sparse instance recognition (SIR) module. SIR first groups the points into instances and then applies instance-wise feature extraction and prediction. In this way, SIR resolves the issue of center feature missing, which hinders the design of the fully sparse architecture for all center-based or anchor-based detectors. Moreover, SIR avoids the time-consuming neighbor queries in previous point-based methods by grouping points into instances. We conduct extensive experiments on the large-scale Waymo Open Dataset to reveal the working mechanism of FSD, and state-of-the-art performance is reported. To demonstrate the superiority of FSD in long-range detection, we also conduct experiments on Argoverse 2 Dataset, which has a much larger perception range ($200m$) than Waymo Open Dataset ($75m$). On such a large perception range, FSD achieves state-of-the-art performance and is 2.4$\times$ faster than the dense counterpart. Codes will be released. | null | null |
Diffusion Visual Counterfactual Explanations | https://papers.nips.cc/paper_files/paper/2022/hash/025f7165a452e7d0b57f1397fed3b0fd-Abstract-Conference.html | Maximilian Augustin, Valentyn Boreiko, Francesco Croce, Matthias Hein | https://papers.nips.cc/paper_files/paper/2022/hash/025f7165a452e7d0b57f1397fed3b0fd-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17480-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/025f7165a452e7d0b57f1397fed3b0fd-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/025f7165a452e7d0b57f1397fed3b0fd-Supplemental-Conference.zip | Visual Counterfactual Explanations (VCEs) are an important tool to understand the decisions of an image classifier. They are “small” but “realistic” semantic changes of the image changing the classifier decision. Current approaches for the generation of VCEs are restricted to adversarially robust models and often contain non-realistic artefacts, or are limited to image classification problems with few classes. In this paper, we overcome this by generating Diffusion Visual Counterfactual Explanations (DVCEs) for arbitrary ImageNet classifiers via a diffusion process. Two modifications to the diffusion process are key for our DVCEs: first, an adaptive parameterization, whose hyperparameters generalize across images and models, together with distance regularization and late start of the diffusion process, allow us to generate images with minimal semantic changes to the original ones but different classification. Second, our cone regularization via an adversarially robust model ensures that the diffusion process does not converge to trivial non-semantic changes, but instead produces realistic images of the target class which achieve high confidence by the classifier. | null | null |
Recurrent Video Restoration Transformer with Guided Deformable Attention | https://papers.nips.cc/paper_files/paper/2022/hash/02687e7b22abc64e651be8da74ec610e-Abstract-Conference.html | Jingyun Liang, Yuchen Fan, Xiaoyu Xiang, Rakesh Ranjan, Eddy Ilg, Simon Green, Jiezhang Cao, Kai Zhang, Radu Timofte, Luc V Gool | https://papers.nips.cc/paper_files/paper/2022/hash/02687e7b22abc64e651be8da74ec610e-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17283-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/02687e7b22abc64e651be8da74ec610e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/02687e7b22abc64e651be8da74ec610e-Supplemental-Conference.pdf | Video restoration aims at restoring multiple high-quality frames from multiple low-quality frames. Existing video restoration methods generally fall into two extreme cases, i.e., they either restore all frames in parallel or restore the video frame by frame in a recurrent way, which would result in different merits and drawbacks. Typically, the former has the advantage of temporal information fusion. However, it suffers from large model size and intensive memory consumption; the latter has a relatively small model size as it shares parameters across frames; however, it lacks long-range dependency modeling ability and parallelizability. In this paper, we attempt to integrate the advantages of the two cases by proposing a recurrent video restoration transformer, namely RVRT. RVRT processes local neighboring frames in parallel within a globally recurrent framework which can achieve a good trade-off between model size, effectiveness, and efficiency. Specifically, RVRT divides the video into multiple clips and uses the previously inferred clip feature to estimate the subsequent clip feature. Within each clip, different frame features are jointly updated with implicit feature aggregation. Across different clips, the guided deformable attention is designed for clip-to-clip alignment, which predicts multiple relevant locations from the whole inferred clip and aggregates their features by the attention mechanism. Extensive experiments on video super-resolution, deblurring, and denoising show that the proposed RVRT achieves state-of-the-art performance on benchmark datasets with balanced model size, testing memory and runtime. | null | null |
A Consolidated Cross-Validation Algorithm for Support Vector Machines via Data Reduction | https://papers.nips.cc/paper_files/paper/2022/hash/026aff87942ce636ada884d934cde0ae-Abstract-Conference.html | Boxiang Wang, Archer Yang | https://papers.nips.cc/paper_files/paper/2022/hash/026aff87942ce636ada884d934cde0ae-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17111-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/026aff87942ce636ada884d934cde0ae-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/026aff87942ce636ada884d934cde0ae-Supplemental-Conference.pdf | We propose a consolidated cross-validation (CV) algorithm for training and tuning the support vector machines (SVM) on reproducing kernel Hilbert spaces. Our consolidated CV algorithm utilizes a recently proposed exact leave-one-out formula for the SVM and accelerates the SVM computation via a data reduction strategy. In addition, to compute the SVM with the bias term (intercept), which is not handled by the existing data reduction methods, we propose a novel two-stage consolidated CV algorithm. With numerical studies, we demonstrate that our algorithm is about an order of magnitude faster than the two mainstream SVM solvers, kernlab and LIBSVM, with almost the same accuracy. | null | null |
On-Demand Sampling: Learning Optimally from Multiple Distributions | https://papers.nips.cc/paper_files/paper/2022/hash/02917acec264a52a729b99d9bc857909-Abstract-Conference.html | Nika Haghtalab, Michael Jordan, Eric Zhao | https://papers.nips.cc/paper_files/paper/2022/hash/02917acec264a52a729b99d9bc857909-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18509-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/02917acec264a52a729b99d9bc857909-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/02917acec264a52a729b99d9bc857909-Supplemental-Conference.zip | Societal and real-world considerations such as robustness, fairness, social welfare and multi-agent tradeoffs have given rise to multi-distribution learning paradigms, such as collaborative [Blum et al. 2017], group distributionally robust [Sagawa et al. 2019], and fair federated learning [Mohri et al. 2019]. In each of these settings, a learner seeks to minimize its worstcase loss over a set of $n$ predefined distributions, while using as few samples as possible. In this paper, we establish the optimal sample complexity of these learning paradigms and give algorithms that meet this sample complexity. Importantly, our sample complexity bounds exceed that of the sample complexity of learning a single distribution only by an additive factor of $\frac{n\log(n)}{\epsilon^2}$. These improve upon the best known sample complexity of agnostic federated learning by Mohri et al. 2019 by a multiplicative factor of $n$, the sample complexity of collaborative learning by Nguyen and Zakynthinou 2018 by a multiplicative factor $\frac{\log(n)}{\epsilon^3}$, and give the first sample complexity bounds for the group DRO objective of Sagawa et al. 2019. To achieve optimal sample complexity, our algorithms learn to sample and learn from distributions on demand. Our algorithm design and analysis extends stochastic optimization techniques to solve zero-sum games in a new stochastic setting. | null | null |
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays | https://papers.nips.cc/paper_files/paper/2022/hash/029df12a9363313c3e41047844ecad94-Abstract-Conference.html | Konstantin Mishchenko, Francis Bach, Mathieu Even, Blake E. Woodworth | https://papers.nips.cc/paper_files/paper/2022/hash/029df12a9363313c3e41047844ecad94-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16766-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/029df12a9363313c3e41047844ecad94-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/029df12a9363313c3e41047844ecad94-Supplemental-Conference.pdf | The existing analysis of asynchronous stochastic gradient descent (SGD) degrades dramatically when any delay is large, giving the impression that performance depends primarily on the delay. On the contrary, we prove much better guarantees for the same asynchronous SGD algorithm regardless of the delays in the gradients, depending instead just on the number of parallel devices used to implement the algorithm. Our guarantees are strictly better than the existing analyses, and we also argue that asynchronous SGD outperforms synchronous minibatch SGD in the settings we consider. For our analysis, we introduce a novel recursion based on ``virtual iterates'' and delay-adaptive stepsizes, which allow us to derive state-of-the-art guarantees for both convex and non-convex objectives. | null | null |
Coresets for Relational Data and The Applications | https://papers.nips.cc/paper_files/paper/2022/hash/029f82afd78288059dc946b105c451fd-Abstract-Conference.html | Jiaxiang Chen, Qingyuan Yang, Ruomin Huang, Hu Ding | https://papers.nips.cc/paper_files/paper/2022/hash/029f82afd78288059dc946b105c451fd-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18405-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/029f82afd78288059dc946b105c451fd-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/029f82afd78288059dc946b105c451fd-Supplemental-Conference.zip | A coreset is a small set that can approximately preserve the structure of the original input data set. Therefore we can run our algorithm on a coreset so as to reduce the total computational complexity. Conventional coreset techniques assume that the input data set is available to process explicitly. However, this assumption may not hold in real-world scenarios. In this paper, we consider the problem of coresets construction over relational data. Namely, the data is decoupled into several relational tables, and it could be very expensive to directly materialize the data matrix by joining the tables. We propose a novel approach called ``aggregation tree with pseudo-cube'' that can build a coreset from bottom to up. Moreover, our approach can neatly circumvent several troublesome issues of relational learning problems [Khamis et al., PODS 2019]. Under some mild assumptions, we show that our coreset approach can be applied for the machine learning tasks, such as clustering, logistic regression and SVM. | null | null |
Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief | https://papers.nips.cc/paper_files/paper/2022/hash/03469b1a66e351b18272be23baf3b809-Abstract-Conference.html | Kaiyang Guo, Shao Yunfeng, Yanhui Geng | https://papers.nips.cc/paper_files/paper/2022/hash/03469b1a66e351b18272be23baf3b809-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18773-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/03469b1a66e351b18272be23baf3b809-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/03469b1a66e351b18272be23baf3b809-Supplemental-Conference.pdf | Model-based offline reinforcement learning (RL) aims to find highly rewarding policy, by leveraging a previously collected static dataset and a dynamics model. While the dynamics model learned through reuse of the static dataset, its generalization ability hopefully promotes policy learning if properly utilized. To that end, several works propose to quantify the uncertainty of predicted dynamics, and explicitly apply it to penalize reward. However, as the dynamics and the reward are intrinsically different factors in context of MDP, characterizing the impact of dynamics uncertainty through reward penalty may incur unexpected tradeoff between model utilization and risk avoidance. In this work, we instead maintain a belief distribution over dynamics, and evaluate/optimize policy through biased sampling from the belief. The sampling procedure, biased towards pessimism, is derived based on an alternating Markov game formulation of offline RL. We formally show that the biased sampling naturally induces an updated dynamics belief with policy-dependent reweighting factor, termed Pessimism-Modulated Dynamics Belief. To improve policy, we devise an iterative regularized policy optimization algorithm for the game, with guarantee of monotonous improvement under certain condition. To make practical, we further devise an offline RL algorithm to approximately find the solution. Empirical results show that the proposed approach achieves state-of-the-art performance on a wide range of benchmark tasks. | null | null |
Generating Training Data with Language Models: Towards Zero-Shot Language Understanding | https://papers.nips.cc/paper_files/paper/2022/hash/0346c148ba1c21c6b4780a961ea141dc-Abstract-Conference.html | Yu Meng, Jiaxin Huang, Yu Zhang, Jiawei Han | https://papers.nips.cc/paper_files/paper/2022/hash/0346c148ba1c21c6b4780a961ea141dc-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18696-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0346c148ba1c21c6b4780a961ea141dc-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0346c148ba1c21c6b4780a961ea141dc-Supplemental-Conference.pdf | Pretrained language models (PLMs) have demonstrated remarkable performance in various natural language processing tasks: Unidirectional PLMs (e.g., GPT) are well known for their superior text generation capabilities; bidirectional PLMs (e.g., BERT) have been the prominent choice for natural language understanding (NLU) tasks. While both types of models have achieved promising few-shot learning performance, their potential for zero-shot learning has been underexplored. In this paper, we present a simple approach that uses both types of PLMs for fully zero-shot learning of NLU tasks without requiring any task-specific data: A unidirectional PLM generates class-conditioned texts guided by prompts, which are used as the training data for fine-tuning a bidirectional PLM. With quality training data selected based on the generation probability and regularization techniques (label smoothing and temporal ensembling) applied to the fine-tuning stage for better generalization and stability, our approach demonstrates strong performance across seven classification tasks of the GLUE benchmark (e.g., 72.3/73.8 on MNLI-m/mm and 92.8 on SST-2), significantly outperforming zero-shot prompting methods and achieving even comparable results to strong few-shot approaches using 32 training samples per class. | null | null |
Wavelet Score-Based Generative Modeling | https://papers.nips.cc/paper_files/paper/2022/hash/03474669b759f6d38cdca6fb4eb905f4-Abstract-Conference.html | Florentin Guth, Simon Coste, Valentin De Bortoli, Stephane Mallat | https://papers.nips.cc/paper_files/paper/2022/hash/03474669b759f6d38cdca6fb4eb905f4-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17946-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/03474669b759f6d38cdca6fb4eb905f4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/03474669b759f6d38cdca6fb4eb905f4-Supplemental-Conference.zip | Score-based generative models (SGMs) synthesize new data samples from Gaussian white noise by running a time-reversed Stochastic Differential Equation (SDE) whose drift coefficient depends on some probabilistic score. The discretization of such SDEs typically requires a large number of time steps and hence a high computational cost. This is because of ill-conditioning properties of the score that we analyze mathematically. Previous approaches have relied on multiscale generation to considerably accelerate SGMs. We explain how this acceleration results from an implicit factorization of the data distribution into a product of conditional probabilities of wavelet coefficients across scales. The resulting Wavelet Score-based Generative Model (WSGM) synthesizes wavelet coefficients with the same number of time steps at all scales, and its time complexity therefore grows linearly with the image size. This is proved mathematically for Gaussian distributions, and shown numerically for physical processes at phase transition and natural image datasets. | null | null |
Robust Binary Models by Pruning Randomly-initialized Networks | https://papers.nips.cc/paper_files/paper/2022/hash/035f23c0ac4cf2b73b9365ba5a98ad56-Abstract-Conference.html | Chen Liu, Ziqi Zhao, Sabine Süsstrunk, Mathieu Salzmann | https://papers.nips.cc/paper_files/paper/2022/hash/035f23c0ac4cf2b73b9365ba5a98ad56-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17758-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/035f23c0ac4cf2b73b9365ba5a98ad56-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/035f23c0ac4cf2b73b9365ba5a98ad56-Supplemental-Conference.pdf | Robustness to adversarial attacks was shown to require a larger model capacity, and thus a larger memory footprint. In this paper, we introduce an approach to obtain robust yet compact models by pruning randomly-initialized binary networks. Unlike adversarial training, which learns the model parameters, we initialize the model parameters as either +1 or −1, keep them fixed, and find a subnetwork structure that is robust to attacks. Our method confirms the Strong Lottery Ticket Hypothesis in the presence of adversarial attacks, and extends this to binary networks. Furthermore, it yields more compact networks with competitive performance than existing works by 1) adaptively pruning different network layers; 2) exploiting an effective binary initialization scheme; 3) incorporating a last batch normalization layer to improve training stability. Our experiments demonstrate that our approach not only always outperforms the state-of-the-art robust binary networks, but also can achieve accuracy better than full-precision ones on some datasets. Finally, we show the structured patterns of our pruned binary networks. | null | null |
Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses | https://papers.nips.cc/paper_files/paper/2022/hash/03a90e1bb2ceb2ea165424f2d96aa3a1-Abstract-Conference.html | Yuzhou Cao, Tianchi Cai, Lei Feng, Lihong Gu, Jinjie GU, Bo An, Gang Niu, Masashi Sugiyama | https://papers.nips.cc/paper_files/paper/2022/hash/03a90e1bb2ceb2ea165424f2d96aa3a1-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18513-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/03a90e1bb2ceb2ea165424f2d96aa3a1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/03a90e1bb2ceb2ea165424f2d96aa3a1-Supplemental-Conference.pdf | \emph{Classification with rejection} (CwR) refrains from making a prediction to avoid critical misclassification when encountering test samples that are difficult to classify. Though previous methods for CwR have been provided with theoretical guarantees, they are only compatible with certain loss functions, making them not flexible enough when the loss needs to be changed with the dataset in practice. In this paper, we derive a novel formulation for CwR that can be equipped with arbitrary loss functions while maintaining the theoretical guarantees. First, we show that $K$-class CwR is equivalent to a $(K\!+\!1)$-class classification problem on the original data distribution with an augmented class, and propose an empirical risk minimization formulation to solve this problem with an estimation error bound. Then, we find necessary and sufficient conditions for the learning \emph{consistency} of the surrogates constructed on our proposed formulation equipped with any classification-calibrated multi-class losses, where consistency means the surrogate risk minimization implies the target risk minimization for CwR. Finally, experiments on benchmark datasets validate the effectiveness of our proposed method. | null | null |
Markovian Interference in Experiments | https://papers.nips.cc/paper_files/paper/2022/hash/03a9a9c1e15850439653bb971a4ad4b3-Abstract-Conference.html | Vivek Farias, Andrew Li, Tianyi Peng, Andrew Zheng | https://papers.nips.cc/paper_files/paper/2022/hash/03a9a9c1e15850439653bb971a4ad4b3-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17906-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/03a9a9c1e15850439653bb971a4ad4b3-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/03a9a9c1e15850439653bb971a4ad4b3-Supplemental-Conference.zip | We consider experiments in dynamical systems where interventions on some experimental units impact other units through a limiting constraint (such as a limited supply of products). Despite outsize practical importance, the best estimators for this `Markovian' interference problem are largely heuristic in nature, and their bias is not well understood. We formalize the problem of inference in such experiments as one of policy evaluation. Off-policy estimators, while unbiased, apparently incur a large penalty in variance relative to state-of-the-art heuristics. We introduce an on-policy estimator: the Differences-In-Q's (DQ) estimator. We show that the DQ estimator can in general have exponentially smaller variance than off-policy evaluation. At the same time, its bias is second order in the impact of the intervention. This yields a striking bias-variance tradeoff so that the DQ estimator effectively dominates state-of-the-art alternatives. From a theoretical perspective, we introduce three separate novel techniques that are of independent interest in the theory of Reinforcement Learning (RL). Our empirical evaluation includes a set of experiments on a city-scale ride-hailing simulator. | null | null |
Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning | https://papers.nips.cc/paper_files/paper/2022/hash/03bdba50e3741ac5e3eaa0e55423587e-Abstract-Conference.html | Paul Rolland, Luca Viano, Norman Schürhoff, Boris Nikolov, Volkan Cevher | https://papers.nips.cc/paper_files/paper/2022/hash/03bdba50e3741ac5e3eaa0e55423587e-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18073-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/03bdba50e3741ac5e3eaa0e55423587e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/03bdba50e3741ac5e3eaa0e55423587e-Supplemental-Conference.zip | While Reinforcement Learning (RL) aims to train an agent from a reward function in a given environment, Inverse Reinforcement Learning (IRL) seeks to recover the reward function from observing an expert's behavior. It is well known that, in general, various reward functions can lead to the same optimal policy, and hence, IRL is ill-defined. However, \cite{cao2021identifiability} showed that, if we observe two or more experts with different discount factors or acting in different environments, the reward function can under certain conditions be identified up to a constant. This work starts by showing an equivalent identifiability statement from multiple experts in tabular MDPs based on a rank condition, which is easily verifiable and is shown to be also necessary. We then extend our result to various different scenarios, i.e., we characterize reward identifiability in the case where the reward function can be represented as a linear combination of given features, making it more interpretable, or when we have access to approximate transition matrices. Even when the reward is not identifiable, we provide conditions characterizing when data on multiple experts in a given environment allows to generalize and train an optimal agent in a new environment. Our theoretical results on reward identifiability and generalizability are validated in various numerical experiments. | null | null |
Parallel Tempering With a Variational Reference | https://papers.nips.cc/paper_files/paper/2022/hash/03cd3cf3f74d4f9ce5958de269960884-Abstract-Conference.html | Nikola Surjanovic, Saifuddin Syed, Alexandre Bouchard-Côté, Trevor Campbell | https://papers.nips.cc/paper_files/paper/2022/hash/03cd3cf3f74d4f9ce5958de269960884-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18401-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/03cd3cf3f74d4f9ce5958de269960884-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/03cd3cf3f74d4f9ce5958de269960884-Supplemental-Conference.pdf | Sampling from complex target distributions is a challenging task fundamental to Bayesian inference. Parallel tempering (PT) addresses this problem by constructing a Markov chain on the expanded state space of a sequence of distributions interpolating between the posterior distribution and a fixed reference distribution, which is typically chosen to be the prior. However, in the typical case where the prior and posterior are nearly mutually singular, PT methods are computationally prohibitive. In this work we address this challenge by constructing a generalized annealing path connecting the posterior to an adaptively tuned variational reference. The reference distribution is tuned to minimize the forward (inclusive) KL divergence to the posterior distribution using a simple, gradient-free moment-matching procedure. We show that our adaptive procedure converges to the forward KL minimizer, and that the forward KL divergence serves as a good proxy to a previously developed measure of PT performance. We also show that in the large-data limit in typical Bayesian models, the proposed method improves in performance, while traditional PT deteriorates arbitrarily. Finally, we introduce PT with two references---one fixed, one variational---with a novel split annealing path that ensures stable variational reference adaptation. The paper concludes with experiments that demonstrate the large empirical gains achieved by our method in a wide range of realistic Bayesian inference scenarios. | null | null |
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems | https://papers.nips.cc/paper_files/paper/2022/hash/03d7e13f0092405804f3a381ade8f3f0-Abstract-Conference.html | Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun | https://papers.nips.cc/paper_files/paper/2022/hash/03d7e13f0092405804f3a381ade8f3f0-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18431-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/03d7e13f0092405804f3a381ade8f3f0-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/03d7e13f0092405804f3a381ade8f3f0-Supplemental-Conference.pdf | We study Reinforcement Learning for partially observable systems using function approximation. We propose a new PO-bilinear framework, that is general enough to include models such as undercomplete tabular Partially Observable Markov Decision Processes (POMDPs), Linear Quadratic Gaussian (LQG), Predictive State Representations (PSRs), as well as a newly introduced model Hilbert Space Embeddings of POMDPs. Under this framework, we propose an actor-critic style algorithm that is capable to performing agnostic policy learning. Given a policy class that consists of memory based policies (i.e., policy that looks at a fixed-length window of recent observations), and a value function class that consists of functions taking both memory and future observations as inputs, our algorithm learns to compete against the best memory-based policy among the policy class. For certain examples such as undercomplete POMDPs and LQGs, by leveraging their special properties, our algorithm is even capable of competing against the globally optimal policy without paying an exponential dependence on the horizon. | null | null |
Off-Policy Evaluation for Episodic Partially Observable Markov Decision Processes under Non-Parametric Models | https://papers.nips.cc/paper_files/paper/2022/hash/03dfa2a7755635f756b160e9f4c6b789-Abstract-Conference.html | Rui Miao, Zhengling Qi, Xiaoke Zhang | https://papers.nips.cc/paper_files/paper/2022/hash/03dfa2a7755635f756b160e9f4c6b789-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17323-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/03dfa2a7755635f756b160e9f4c6b789-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/03dfa2a7755635f756b160e9f4c6b789-Supplemental-Conference.zip | We study the problem of off-policy evaluation (OPE) for episodic Partially Observable Markov Decision Processes (POMDPs) with continuous states. Motivated by the recently proposed proximal causal inference framework, we develop a non-parametric identification result for estimating the policy value via a sequence of so-called V-bridge functions with the help of time-dependent proxy variables. We then develop a fitted-Q-evaluation-type algorithm to estimate V-bridge functions recursively, where a non-parametric instrumental variable (NPIV) problem is solved at each step. By analyzing this challenging sequential NPIV estimation, we establish the finite-sample error bounds for estimating the V-bridge functions and accordingly that for evaluating the policy value, in terms of the sample size, length of horizon and so-called (local) measure of ill-posedness at each step. To the best of our knowledge, this is the first finite-sample error bound for OPE in POMDPs under non-parametric models. | null | null |
Efficient Knowledge Distillation from Model Checkpoints | https://papers.nips.cc/paper_files/paper/2022/hash/03e0712bf85ebe7cec4f1a7fc53216c9-Abstract-Conference.html | Chaofei Wang, Qisen Yang, Rui Huang, Shiji Song, Gao Huang | https://papers.nips.cc/paper_files/paper/2022/hash/03e0712bf85ebe7cec4f1a7fc53216c9-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18645-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/03e0712bf85ebe7cec4f1a7fc53216c9-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/03e0712bf85ebe7cec4f1a7fc53216c9-Supplemental-Conference.pdf | Knowledge distillation is an effective approach to learn compact models (students) with the supervision of large and strong models (teachers). As empirically there exists a strong correlation between the performance of teacher and student models, it is commonly believed that a high performing teacher is preferred. Consequently, practitioners tend to use a well trained network or an ensemble of them as the teacher. In this paper, we observe that an intermediate model, i.e., a checkpoint in the middle of the training procedure, often serves as a better teacher compared to the fully converged model, although the former has much lower accuracy. More surprisingly, a weak snapshot ensemble of several intermediate models from a same training trajectory can outperform a strong ensemble of independently trained and fully converged models, when they are used as teachers. We show that this phenomenon can be partially explained by the information bottleneck principle: the feature representations of intermediate models can have higher mutual information regarding the input, and thus contain more ``dark knowledge'' for effective distillation. We further propose an optimal intermediate teacher selection algorithm based on maximizing the total task-related mutual information. Experiments verify its effectiveness and applicability. Our code is available at https://github.com/LeapLabTHU/CheckpointKD. | null | null |
Decoupled Self-supervised Learning for Graphs | https://papers.nips.cc/paper_files/paper/2022/hash/040c816286b3844fd78f2124eec75f2e-Abstract-Conference.html | Teng Xiao, Zhengyu Chen, Zhimeng Guo, Zeyang Zhuang, Suhang Wang | https://papers.nips.cc/paper_files/paper/2022/hash/040c816286b3844fd78f2124eec75f2e-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18228-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/040c816286b3844fd78f2124eec75f2e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/040c816286b3844fd78f2124eec75f2e-Supplemental-Conference.zip | This paper studies the problem of conducting self-supervised learning for node representation learning on graphs. Most existing self-supervised learning methods assume the graph is homophilous, where linked nodes often belong to the same class or have similar features. However, such assumptions of homophily do not always hold in real-world graphs. We address this problem by developing a decoupled self-supervised learning (DSSL) framework for graph neural networks. DSSL imitates a generative process of nodes and links from latent variable modeling of the semantic structure, which decouples different underlying semantics between different neighborhoods into the self-supervised learning process. Our DSSL framework is agnostic to the encoders and does not need prefabricated augmentations, thus is flexible to different graphs. To effectively optimize the framework, we derive the evidence lower bound of the self-supervised objective and develop a scalable training algorithm with variational inference. We provide a theoretical analysis to justify that DSSL enjoys the better downstream performance. Extensive experiments on various types of graph benchmarks demonstrate that our proposed framework can achieve better performance compared with competitive baselines. | null | null |
Shadow Knowledge Distillation: Bridging Offline and Online Knowledge Transfer | https://papers.nips.cc/paper_files/paper/2022/hash/040d3b6af368bf71f952c18da5713b48-Abstract-Conference.html | Lujun Li, ZHE JIN | https://papers.nips.cc/paper_files/paper/2022/hash/040d3b6af368bf71f952c18da5713b48-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16803-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/040d3b6af368bf71f952c18da5713b48-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/040d3b6af368bf71f952c18da5713b48-Supplemental-Conference.pdf | Knowledge distillation can be generally divided into offline and online categories according to whether teacher model is pre-trained and persistent during the distillation process. Offline distillation can employ existing models yet always demonstrates inferior performance than online ones. In this paper, we first empirically show that the essential factor for their performance gap lies in the reversed distillation from student to teacher, rather than the training fashion. Offline distillation can achieve competitive performance gain by fine-tuning pre-trained teacher to adapt student with such reversed distillation. However, this fine-tuning process still costs lots of training budgets. To alleviate this dilemma, we propose SHAKE, a simple yet effective SHAdow KnowlEdge transfer framework to bridge offline and online distillation, which trades the accuracy with efficiency. Specifically, we build an extra shadow head on the backbone to mimic the predictions of pre-trained teacher as its shadow. Then, this shadow head is leveraged as a proxy teacher to perform bidirectional distillation with student on the fly. In this way, SHAKE not only updates this student-aware proxy teacher with the knowledge of pre-trained model, but also greatly optimizes costs of augmented reversed distillation. Extensive experiments on classification and object detection tasks demonstrate that our technique achieves state-of-the-art results with different CNNs and Vision Transformer models. Additionally, our method shows strong compatibility with multi-teacher and augmentation strategies by gaining additional performance improvement. Code is made publicly available at https://lilujunai.github.io/SHAKE/. | null | null |
ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs | https://papers.nips.cc/paper_files/paper/2022/hash/0418973e545b932939302cb605d06f43-Abstract-Conference.html | Limei Wang, Yi Liu, Yuchao Lin, Haoran Liu, Shuiwang Ji | https://papers.nips.cc/paper_files/paper/2022/hash/0418973e545b932939302cb605d06f43-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17348-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0418973e545b932939302cb605d06f43-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0418973e545b932939302cb605d06f43-Supplemental-Conference.zip | Many real-world data can be modeled as 3D graphs, but learning representations that incorporates 3D information completely and efficiently is challenging. Existing methods either use partial 3D information, or suffer from excessive computational cost. To incorporate 3D information completely and efficiently, we propose a novel message passing scheme that operates within 1-hop neighborhood. Our method guarantees full completeness of 3D information on 3D graphs by achieving global and local completeness. Notably, we propose the important rotation angles to fulfill global completeness. Additionally, we show that our method is orders of magnitude faster than prior methods. We provide rigorous proof of completeness and analysis of time complexity for our methods. As molecules are in essence quantum systems, we build the \underline{com}plete and \underline{e}fficient graph neural network (ComENet) by combing quantum inspired basis functions and the proposed message passing scheme. Experimental results demonstrate the capability and efficiency of ComENet, especially on real-world datasets that are large in both numbers and sizes of graphs. Our code is publicly available as part of the DIG library (\url{https://github.com/divelab/DIG}). | null | null |
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret | https://papers.nips.cc/paper_files/paper/2022/hash/0463ec87d0ac1e98a6cbe3d95d4e3e35-Abstract-Conference.html | Jiawei Huang, Li Zhao, Tao Qin, Wei Chen, Nan Jiang, Tie-Yan Liu | https://papers.nips.cc/paper_files/paper/2022/hash/0463ec87d0ac1e98a6cbe3d95d4e3e35-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16742-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0463ec87d0ac1e98a6cbe3d95d4e3e35-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0463ec87d0ac1e98a6cbe3d95d4e3e35-Supplemental-Conference.pdf | We propose a new learning framework that captures the tiered structure of many real-world user-interaction applications, where the users can be divided into two groups based on their different tolerance on exploration risks and should be treated separately. In this setting, we simultaneously maintain two policies $\pi^{\text{O}}$ and $\pi^{\text{E}}$: $\pi^{\text{O}}$ (``O'' for ``online'') interacts with more risk-tolerant users from the first tier and minimizes regret by balancing exploration and exploitation as usual, while $\pi^{\text{E}}$ (``E'' for ``exploit'') exclusively focuses on exploitation for risk-averse users from the second tier utilizing the data collected so far. An important question is whether such a separation yields advantages over the standard online setting (i.e., $\pi^{\text{E}}=\pi^{\text{O}}$) for the risk-averse users. We individually consider the gap-independent vs.~gap-dependent settings. For the former, we prove that the separation is indeed not beneficial from a minimax perspective. For the latter, we show that if choosing Pessimistic Value Iteration as the exploitation algorithm to produce $\pi^{\text{E}}$, we can achieve a constant regret for risk-averse users independent of the number of episodes $K$, which is in sharp contrast to the $\Omega(\log K)$ regret for any online RL algorithms in the same setting, while the regret of $\pi^{\text{O}}$ (almost) maintains its online regret optimality and does not need to compromise for the success of $\pi^{\text{E}}$. | null | null |
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness | https://papers.nips.cc/paper_files/paper/2022/hash/047aa59e51e3ac7a2422a55468feefd5-Abstract-Conference.html | Sarah Sachs, Hedi Hadiji, Tim van Erven, Cristóbal Guzmán | https://papers.nips.cc/paper_files/paper/2022/hash/047aa59e51e3ac7a2422a55468feefd5-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18709-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/047aa59e51e3ac7a2422a55468feefd5-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/047aa59e51e3ac7a2422a55468feefd5-Supplemental-Conference.pdf | Stochastic and adversarial data are two widely studied settings in online learning. But many optimizationtasks are neither i.i.d. nor fully adversarial, which makes it of fundamental interest to get a better theoretical understanding of the world between these extremes. In this work we establish novel regret bounds for online convex optimization in a setting that interpolates between stochastic i.i.d. and fully adversarial losses. By exploiting smoothness of the expected losses, these bounds replace a dependence on the maximum gradient length by the variance of the gradients, which was previously known only for linear losses. In addition, they weaken the i.i.d. assumption by allowing, for example, adversarially poisoned rounds, which were previously considered in the expert and bandit setting. Our results extend this to the online convex optimization framework. In the fully i.i.d. case, our bounds match the rates one would expect from results in stochastic acceleration, and in the fully adversarial case they gracefully deteriorate to match the minimax regret. We further provide lower bounds showing that our regret upper bounds aretight for all intermediate regimes in terms of the stochastic variance and theadversarial variation of the loss gradients. | null | null |
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence) | https://papers.nips.cc/paper_files/paper/2022/hash/04b42392f9a3a16aea012395359b8148-Abstract-Conference.html | Jiayuan Ye, Reza Shokri | https://papers.nips.cc/paper_files/paper/2022/hash/04b42392f9a3a16aea012395359b8148-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16726-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/04b42392f9a3a16aea012395359b8148-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/04b42392f9a3a16aea012395359b8148-Supplemental-Conference.pdf | Prior work on differential privacy analysis of randomized SGD algorithms relies on composition theorems, where the implicit (unrealistic) assumption is that the internal state of the iterative algorithm is revealed to the adversary. As a result, the R\'enyi DP bounds derived by such composition-based analyses linearly grow with the number of training epochs. When the internal state of the algorithm is hidden, we prove a converging privacy bound for noisy stochastic gradient descent (on strongly convex smooth loss functions). We show how to take advantage of privacy amplification by sub-sampling and randomized post-processing, and prove the dynamics of privacy bound for shuffle and partition'' andsample without replacement'' stochastic mini-batch gradient descent schemes. We prove that, in these settings, our privacy bound converges exponentially fast and is substantially smaller than the composition bounds, notably after a few number of training epochs. Thus, unless the DP algorithm converges fast, our privacy analysis shows that hidden state analysis can significantly amplify differential privacy. | null | null |
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling | https://papers.nips.cc/paper_files/paper/2022/hash/04bd683d5428d91c5fbb5a7d2c27064d-Abstract-Conference.html | Gabriel Cardoso, Sergey Samsonov, Achille Thin, Eric Moulines, Jimmy Olsson | https://papers.nips.cc/paper_files/paper/2022/hash/04bd683d5428d91c5fbb5a7d2c27064d-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17244-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/04bd683d5428d91c5fbb5a7d2c27064d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/04bd683d5428d91c5fbb5a7d2c27064d-Supplemental-Conference.pdf | Importance Sampling (IS) is a method for approximating expectations with respect to a target distribution using independent samples from a proposal distribution and the associated to importance weights. In many cases, the target distribution is known up to a normalization constant and self-normalized IS (SNIS) is then used. While the use of self-normalization can have a positive effect on the dispersion of the estimator, it introduces bias. In this work, we propose a new method BR-SNIS whose complexity is essentially the same as SNIS and which significantly reduces bias. This method is a wrapper, in the sense that it uses the same proposal samples and importance weights but makes a clever use of iterated sampling-importance-resampling (i-SIR) to form a bias-reduced version of the estimator. We derive the proposed algorithm with rigorous theoretical results, including novel bias, variance, and high-probability bounds. We illustrate our findings with numerical examples. | null | null |
Learning to Configure Computer Networks with Neural Algorithmic Reasoning | https://papers.nips.cc/paper_files/paper/2022/hash/04cc90ec6868b97b7423dc38ced1e35c-Abstract-Conference.html | Luca Beurer-Kellner, Martin Vechev, Laurent Vanbever, Petar Veličković | https://papers.nips.cc/paper_files/paper/2022/hash/04cc90ec6868b97b7423dc38ced1e35c-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16789-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/04cc90ec6868b97b7423dc38ced1e35c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/04cc90ec6868b97b7423dc38ced1e35c-Supplemental-Conference.zip | We present a new method for scaling automatic configuration of computer networks. The key idea is to relax the computationally hard search problem of finding a configuration that satisfies a given specification into an approximate objective amenable to learning-based techniques. Based on this idea, we train a neural algorithmic model which learns to generate configurations likely to (fully or partially) satisfy a given specification under existing routing protocols. By relaxing the rigid satisfaction guarantees, our approach (i) enables greater flexibility: it is protocol-agnostic, enables cross-protocol reasoning, and does not depend on hardcoded rules; and (ii) finds configurations for much larger computer networks than previously possible. Our learned synthesizer is up to 490x faster than state-of-the-art SMT-based methods, while producing configurations which on average satisfy more than 93% of the provided requirements. | null | null |
Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks | https://papers.nips.cc/paper_files/paper/2022/hash/04cda3a5ef307978cb5dbef6ab649380-Abstract-Conference.html | Mingze Wang, Chao Ma | https://papers.nips.cc/paper_files/paper/2022/hash/04cda3a5ef307978cb5dbef6ab649380-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19135-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/04cda3a5ef307978cb5dbef6ab649380-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/04cda3a5ef307978cb5dbef6ab649380-Supplemental-Conference.zip | The convergence of GD and SGD when training mildly parameterized neural networks starting from random initialization is studied. For a broad range of models and loss functions, including the widely used square loss and cross entropy loss, we prove an ''early stage convergence'' result. We show that the loss is decreased by a significant amount in the early stage of the training, and this decreasing is fast. Furthurmore, for exponential type loss functions, and under some assumptions on the training data, we show global convergence of GD. Instead of relying on extreme over-parameterization, our study is based on a microscopic analysis of the activation patterns for the neurons, which helps us derive gradient lower bounds. The results on activation patterns, which we call ``neuron partition'', help build intuitions for understanding the behavior of neural networks' training dynamics, and may be of independent interest. | null | null |
On Divergence Measures for Bayesian Pseudocoresets | https://papers.nips.cc/paper_files/paper/2022/hash/04f8311e7e22eac15d67fe45c242ead8-Abstract-Conference.html | Balhae Kim, Jungwon Choi, Seanie Lee, Yoonho Lee, Jung-Woo Ha, Juho Lee | https://papers.nips.cc/paper_files/paper/2022/hash/04f8311e7e22eac15d67fe45c242ead8-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17012-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/04f8311e7e22eac15d67fe45c242ead8-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/04f8311e7e22eac15d67fe45c242ead8-Supplemental-Conference.pdf | A Bayesian pseudocoreset is a small synthetic dataset for which the posterior over parameters approximates that of the original dataset. While promising, the scalability of Bayesian pseudocoresets is not yet validated in large-scale problems such as image classification with deep neural networks. On the other hand, dataset distillation methods similarly construct a small dataset such that the optimization with the synthetic dataset converges to a solution similar to optimization with full data. Although dataset distillation has been empirically verified in large-scale settings, the framework is restricted to point estimates, and their adaptation to Bayesian inference has not been explored. This paper casts two representative dataset distillation algorithms as approximations to methods for constructing pseudocoresets by minimizing specific divergence measures: reverse KL divergence and Wasserstein distance. Furthermore, we provide a unifying view of such divergence measures in Bayesian pseudocoreset construction. Finally, we propose a novel Bayesian pseudocoreset algorithm based on minimizing forward KL divergence. Our empirical results demonstrate that the pseudocoresets constructed from these methods reflect the true posterior even in large-scale Bayesian inference problems. | null | null |
Unsupervised Learning of Equivariant Structure from Sequences | https://papers.nips.cc/paper_files/paper/2022/hash/0503f5dce343a1d06d16ba103dd52db1-Abstract-Conference.html | Takeru Miyato, Masanori Koyama, Kenji Fukumizu | https://papers.nips.cc/paper_files/paper/2022/hash/0503f5dce343a1d06d16ba103dd52db1-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16982-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0503f5dce343a1d06d16ba103dd52db1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0503f5dce343a1d06d16ba103dd52db1-Supplemental-Conference.pdf | In this study, we present \textit{meta-sequential prediction} (MSP), an unsupervised framework to learn the symmetry from the time sequence of length at least three. Our method leverages the stationary property~(e.g. constant velocity, constant acceleration) of the time sequence to learn the underlying equivariant structure of the dataset by simply training the encoder-decoder model to be able to predict the future observations. We will demonstrate that, with our framework, the hidden disentangled structure of the dataset naturally emerges as a by-product by applying \textit{simultaneous block-diagonalization} to the transition operators in the latent space, the procedure which is commonly used in representation theory to decompose the feature-space based on the type of response to group actions.We will showcase our method from both empirical and theoretical perspectives.Our result suggests that finding a simple structured relation and learning a model with extrapolation capability are two sides of the same coin. The code is available at https://github.com/takerum/metasequentialprediction. | null | null |
Multi-Class $H$-Consistency Bounds | https://papers.nips.cc/paper_files/paper/2022/hash/051f3997af1dd65da8e14397b6a72f8e-Abstract-Conference.html | Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong | https://papers.nips.cc/paper_files/paper/2022/hash/051f3997af1dd65da8e14397b6a72f8e-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17197-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/051f3997af1dd65da8e14397b6a72f8e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/051f3997af1dd65da8e14397b6a72f8e-Supplemental-Conference.pdf | We present an extensive study of $H$-consistency bounds for multi-class classification. These are upper bounds on the target loss estimation error of a predictor in a hypothesis set $H$, expressed in terms of the surrogate loss estimation error of that predictor. They are stronger and more significant guarantees than Bayes-consistency, $H$-calibration or $H$-consistency, and more informative than excess error bounds derived for $H$ being the family of all measurable functions. We give a series of new $H$-consistency bounds for surrogate multi-class losses, including max losses, sum losses, and constrained losses, both in the non-adversarial and adversarial cases, and for different differentiable or convex auxiliary functions used. We also prove that no non-trivial $H$-consistency bound can be given in some cases. To our knowledge, these are the first $H$-consistency bounds proven for the multi-class setting. Our proof techniques are also novel and likely to be useful in the analysis of other such guarantees. | null | null |
On the Frequency-bias of Coordinate-MLPs | https://papers.nips.cc/paper_files/paper/2022/hash/0525fa17a8dbea687359116d01732e12-Abstract-Conference.html | Sameera Ramasinghe, Lachlan E. MacDonald, Simon Lucey | https://papers.nips.cc/paper_files/paper/2022/hash/0525fa17a8dbea687359116d01732e12-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17312-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0525fa17a8dbea687359116d01732e12-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0525fa17a8dbea687359116d01732e12-Supplemental-Conference.pdf | We show that typical implicit regularization assumptions for deep neural networks (for regression) do not hold for coordinate-MLPs, a family of MLPs that are now ubiquitous in computer vision for representing high-frequency signals. Lack of such implicit bias disrupts smooth interpolations between training samples, and hampers generalizing across signal regions with different spectra. We investigate this behavior through a Fourier lens and uncover that as the bandwidth of a coordinate-MLP is enhanced, lower frequencies tend to get suppressed unless a suitable prior is provided explicitly. Based on these insights, we propose a simple regularization technique that can mitigate the above problem, which can be incorporated into existing networks without any architectural modifications. | null | null |
Mask Matching Transformer for Few-Shot Segmentation | https://papers.nips.cc/paper_files/paper/2022/hash/053a18c03e0844d0c484ba2861f8ae6c-Abstract-Conference.html | siyu jiao, Gengwei Zhang, Shant Navasardyan, Ling Chen, Yao Zhao, Yunchao Wei, Humphrey Shi | https://papers.nips.cc/paper_files/paper/2022/hash/053a18c03e0844d0c484ba2861f8ae6c-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16678-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/053a18c03e0844d0c484ba2861f8ae6c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/053a18c03e0844d0c484ba2861f8ae6c-Supplemental-Conference.zip | In this paper, we aim to tackle the challenging few-shot segmentation task from a new perspective. Typical methods follow the paradigm to firstly learn prototypical features from support images and then match query features in pixel-level to obtain segmentation results. However, to obtain satisfactory segments, such a paradigm needs to couple the learning of the matching operations with heavy segmentation modules, limiting the flexibility of design and increasing the learning complexity. To alleviate this issue, we propose Mask Matching Transformer (MM-Former), a new paradigm for the few-shot segmentation task. Specifically, MM-Former first uses a class-agnostic segmenter to decompose the query image into multiple segment proposals. Then, a simple matching mechanism is applied to merge the related segment proposals into the final mask guided by the support images. The advantages of our MM-Former are two-fold. First, the MM-Former follows the paradigm of 'decompose first and then blend', allowing our method to benefit from the advanced potential objects segmenter to produce high-quality mask proposals for query images. Second, the mission of prototypical features is relaxed to learn coefficients to fuse correct ones within a proposal pool, making the MM-Former be well generalized to complex scenarios or cases. We conduct extensive experiments on the popular COCO-$20^i$ and Pascal-$5^i$ benchmarks. Competitive results well demonstrate the effectiveness and the generalization ability of our MM-Former. Code is available at https://github.com/Picsart-AI-Research/Mask-Matching-Transformer. | null | null |
Queue Up Your Regrets: Achieving the Dynamic Capacity Region of Multiplayer Bandits | https://papers.nips.cc/paper_files/paper/2022/hash/056e8e9c8ca9929cb6cf198952bf1dbb-Abstract-Conference.html | Ilai Bistritz, Nicholas Bambos | https://papers.nips.cc/paper_files/paper/2022/hash/056e8e9c8ca9929cb6cf198952bf1dbb-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18952-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/056e8e9c8ca9929cb6cf198952bf1dbb-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/056e8e9c8ca9929cb6cf198952bf1dbb-Supplemental-Conference.pdf | Abstract Consider $N$ cooperative agents such that for $T$ turns, each agent n takes an action $a_{n}$ and receives a stochastic reward $r_{n}\left(a_{1},\ldots,a_{N}\right)$. Agents cannot observe the actions of other agents and do not know even their own reward function. The agents can communicate with their neighbors on a connected graph $G$ with diameter $d\left(G\right)$. We want each agent $n$ to achieve an expected average reward of at least $\lambda_{n}$ over time, for a given quality of service (QoS) vector $\boldsymbol{\lambda}$. A QoS vector $\boldsymbol{\lambda}$ is not necessarily achievable. By giving up on immediate reward, knowing that the other agents will compensate later, agents can improve their achievable capacity region. Our main observation is that the gap between $\lambda_{n}t$ and the accumulated reward of agent $n$, which we call the QoS regret, behaves like a queue. Inspired by this observation, we propose a distributed algorithm that aims to learn a max-weight matching of agents to actions. In each epoch, the algorithm employs a consensus phase where the agents agree on a certain weighted sum of rewards by communicating only $O\left(d\left(G\right)\right)$ numbers every turn. Then, the algorithm uses distributed successive elimination on a random subset of action profiles to approximately maximize this weighted sum of rewards. We prove a bound on the accumulated sum of expected QoS regrets of all agents, that holds if $\boldsymbol{\lambda}$ is a safety margin $\varepsilon_{T}$ away from the boundary of the capacity region, where $\varepsilon_{T}\rightarrow0$ as $T\rightarrow\infty$. This bound implies that, for large $T$, our algorithm can achieve any $\boldsymbol{\lambda}$ in the interior of the dynamic capacity region, while all agents are guaranteed an empirical average expected QoS regret of $\tilde{O}\left(1\right)$ over $t=1,\ldots,T$ which never exceeds $\tilde{O}\left(\sqrt{t}\right)$ for any $t$. We then extend our result to time-varying i.i.d. communication graphs. | null | null |
Differentially Private Covariance Revisited | https://papers.nips.cc/paper_files/paper/2022/hash/057405fd73dd7ba7f32a7cb34fb7c7f5-Abstract-Conference.html | Wei Dong, Yuting Liang, Ke Yi | https://papers.nips.cc/paper_files/paper/2022/hash/057405fd73dd7ba7f32a7cb34fb7c7f5-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19211-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/057405fd73dd7ba7f32a7cb34fb7c7f5-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/057405fd73dd7ba7f32a7cb34fb7c7f5-Supplemental-Conference.zip | In this paper, we present two new algorithms for covariance estimation under concentrated differential privacy (zCDP). The first algorithm achieves a Frobenius error of $\tilde{O}(d^{1/4}\sqrt{\mathrm{tr}}/\sqrt{n} + \sqrt{d}/n)$, where $\mathrm{tr}$ is the trace of the covariance matrix. By taking $\mathrm{tr}=1$, this also implies a worst-case error bound of $\tilde{O}(d^{1/4}/\sqrt{n})$, which improves the standard Gaussian mechanism's $\tilde{O}(d/n)$ for the regime $d>\widetilde{\Omega}(n^{2/3})$. Our second algorithm offers a tail-sensitive bound that could be much better on skewed data. The corresponding algorithms are also simple and efficient. Experimental results show that they offer significant improvements over prior work. | null | null |
Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model | https://papers.nips.cc/paper_files/paper/2022/hash/05b12f103c9e613efc4c85674cdc9066-Abstract-Conference.html | Pranjal Awasthi, Abhimanyu Das, Weihao Kong, Rajat Sen | https://papers.nips.cc/paper_files/paper/2022/hash/05b12f103c9e613efc4c85674cdc9066-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19284-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/05b12f103c9e613efc4c85674cdc9066-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/05b12f103c9e613efc4c85674cdc9066-Supplemental-Conference.pdf | We study the problem of learning generalized linear models under adversarial corruptions.We analyze a classical heuristic called the \textit{iterative trimmed maximum likelihood estimator} which is known to be effective against \textit{label corruptions} in practice. Under label corruptions, we prove that this simple estimator achieves minimax near-optimal risk on a wide range of generalized linear models, including Gaussian regression, Poisson regression and Binomial regression. Finally, we extend the estimator to the much more challenging setting of \textit{label and covariate corruptions} and demonstrate its robustness and optimality in that setting as well. | null | null |
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error | https://papers.nips.cc/paper_files/paper/2022/hash/05b63fa06784b71aab3939004e0f0a0d-Abstract-Conference.html | Yuqin Yang, AmirEmad Ghassami, Mohamed Nafea, Negar Kiyavash, Kun Zhang, Ilya Shpitser | https://papers.nips.cc/paper_files/paper/2022/hash/05b63fa06784b71aab3939004e0f0a0d-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17005-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/05b63fa06784b71aab3939004e0f0a0d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/05b63fa06784b71aab3939004e0f0a0d-Supplemental-Conference.pdf | We focus on causal discovery in the presence of measurement error in linear systems where the mixing matrix, i.e., the matrix indicating the independent exogenous noise terms pertaining to the observed variables, is identified up to permutation and scaling of the columns. We demonstrate a somewhat surprising connection between this problem and causal discovery in the presence of unobserved parentless causes, in the sense that there is a mapping, given by the mixing matrix, between the underlying models to be inferred in these problems. Consequently, any identifiability result based on the mixing matrix for one model translates to an identifiability result for the other model. We characterize to what extent the causal models can be identified under a two-part faithfulness assumption. Under only the first part of the assumption (corresponding to the conventional definition of faithfulness), the structure can be learned up to the causal ordering among an ordered grouping of the variables but not all the edges across the groups can be identified. We further show that if both parts of the faithfulness assumption are imposed, the structure can be learned up to a more refined ordered grouping. As a result of this refinement, for the latent variable model with unobserved parentless causes, the structure can be identified. Based on our theoretical results, we propose causal structure learning methods for both models, and evaluate their performance on synthetic data. | null | null |
Density-driven Regularization for Out-of-distribution Detection | https://papers.nips.cc/paper_files/paper/2022/hash/05b69cc4c8ff6e24c5de1ecd27223d37-Abstract-Conference.html | Wenjian Huang, Hao Wang, Jiahao Xia, Chengyan Wang, Jianguo Zhang | https://papers.nips.cc/paper_files/paper/2022/hash/05b69cc4c8ff6e24c5de1ecd27223d37-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18816-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/05b69cc4c8ff6e24c5de1ecd27223d37-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/05b69cc4c8ff6e24c5de1ecd27223d37-Supplemental-Conference.pdf | Detecting out-of-distribution (OOD) samples is essential for reliably deploying deep learning classifiers in open-world applications. However, existing detectors relying on discriminative probability suffer from the overconfident posterior estimate for OOD data. Other reported approaches either impose strong unproven parametric assumptions to estimate OOD sample density or develop empirical detectors lacking clear theoretical motivations. To address these issues, we propose a theoretical probabilistic framework for OOD detection in deep classification networks, in which two regularization constraints are constructed to reliably calibrate and estimate sample density to identify OOD. Specifically, the density consistency regularization enforces the agreement between analytical and empirical densities of observable low-dimensional categorical labels. The contrastive distribution regularization separates the densities between in distribution (ID) and distribution-deviated samples. A simple and robust implementation algorithm is also provided, which can be used for any pre-trained neural network classifiers. To the best of our knowledge, we have conducted the most extensive evaluations and comparisons on computer vision benchmarks. The results show that our method significantly outperforms state-of-the-art detectors, and even achieves comparable or better performance than methods utilizing additional large-scale outlier exposure datasets. | null | null |
Sparsity in Continuous-Depth Neural Networks | https://papers.nips.cc/paper_files/paper/2022/hash/0626822954674a06ccd9c234e3f0d572-Abstract-Conference.html | Hananeh Aliee, Till Richter, Mikhail Solonin, Ignacio Ibarra, Fabian Theis, Niki Kilbertus | https://papers.nips.cc/paper_files/paper/2022/hash/0626822954674a06ccd9c234e3f0d572-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19112-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0626822954674a06ccd9c234e3f0d572-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0626822954674a06ccd9c234e3f0d572-Supplemental-Conference.pdf | Neural Ordinary Differential Equations (NODEs) have proven successful in learning dynamical systems in terms of accurately recovering the observed trajectories. While different types of sparsity have been proposed to improve robustness, the generalization properties of NODEs for dynamical systems beyond the observed data are underexplored. We systematically study the influence of weight and feature sparsity on forecasting as well as on identifying the underlying dynamical laws. Besides assessing existing methods, we propose a regularization technique to sparsify ``input-output connections'' and extract relevant features during training. Moreover, we curate real-world datasets including human motion capture and human hematopoiesis single-cell RNA-seq data to realistically analyze different levels of out-of-distribution (OOD) generalization in forecasting and dynamics identification respectively. Our extensive empirical evaluation on these challenging benchmarks suggests that weight sparsity improves generalization in the presence of noise or irregular sampling. However, it does not prevent learning spurious feature dependencies in the inferred dynamics, rendering them impractical for predictions under interventions, or for inferring the true underlying dynamics. Instead, feature sparsity can indeed help with recovering sparse ground-truth dynamics compared to unregularized NODEs. | null | null |
Environment Diversification with Multi-head Neural Network for Invariant Learning | https://papers.nips.cc/paper_files/paper/2022/hash/062d711fb777322e2152435459e6e9d9-Abstract-Conference.html | Bo-Wei Huang, Keng-Te Liao, Chang-Sheng Kao, Shou-De Lin | https://papers.nips.cc/paper_files/paper/2022/hash/062d711fb777322e2152435459e6e9d9-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18268-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/062d711fb777322e2152435459e6e9d9-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/062d711fb777322e2152435459e6e9d9-Supplemental-Conference.pdf | Neural networks are often trained with empirical risk minimization; however, it has been shown that a shift between training and testing distributions can cause unpredictable performance degradation. On this issue, a research direction, invariant learning, has been proposed to extract causal features insensitive to the distributional changes. This work proposes EDNIL, an invariant learning framework containing a multi-head neural network to absorb data biases. We show that this framework does not require prior knowledge about environments or strong assumptions about the pre-trained model. We also reveal that the proposed algorithm has theoretical connections to recent studies discussing properties of variant and invariant features. Finally, we demonstrate that models trained with EDNIL are empirically more robust against distributional shifts. | null | null |
Learning Probabilistic Models from Generator Latent Spaces with Hat EBM | https://papers.nips.cc/paper_files/paper/2022/hash/062f9525a7476942f61a6c3b42d0a63f-Abstract-Conference.html | Mitch Hill, Erik Nijkamp, Jonathan Mitchell, Bo Pang, Song-Chun Zhu | https://papers.nips.cc/paper_files/paper/2022/hash/062f9525a7476942f61a6c3b42d0a63f-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17073-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/062f9525a7476942f61a6c3b42d0a63f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/062f9525a7476942f61a6c3b42d0a63f-Supplemental-Conference.zip | This work proposes a method for using any generator network as the foundation of an Energy-Based Model (EBM). Our formulation posits that observed images are the sum of unobserved latent variables passed through the generator network and a residual random variable that spans the gap between the generator output and the image manifold. One can then define an EBM that includes the generator as part of its forward pass, which we call the Hat EBM. The model can be trained without inferring the latent variables of the observed data or calculating the generator Jacobian determinant. This enables explicit probabilistic modeling of the output distribution of any type of generator network. Experiments show strong performance of the proposed method on (1) unconditional ImageNet synthesis at 128$\times$128 resolution, (2) refining the output of existing generators, and (3) learning EBMs that incorporate non-probabilistic generators. Code and pretrained models to reproduce our results are available at https://github.com/point0bar1/hat-ebm. | null | null |
Learning Best Combination for Efficient N:M Sparsity | https://papers.nips.cc/paper_files/paper/2022/hash/06589ec9d86876508600a678f9c8f51d-Abstract-Conference.html | Yuxin Zhang, Mingbao Lin, ZhiHang Lin, Yiting Luo, Ke Li, Fei Chao, Yongjian Wu, Rongrong Ji | https://papers.nips.cc/paper_files/paper/2022/hash/06589ec9d86876508600a678f9c8f51d-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17822-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/06589ec9d86876508600a678f9c8f51d-Paper-Conference.pdf | null | By forcing N out of M consecutive weights to be non-zero, the recent N:M fine-grained network sparsity has received increasing attention with its two attractive advantages over traditional irregular network sparsity methods: 1) Promising performance at a high sparsity. 2) Significant speedups when performed on NVIDIA A100 GPUs. Current implementation on N:M sparsity requires a tedious pre-training phase or computationally heavy from-scratch training. To circumvent these problems, this paper presents an efficient solution for achieving N:M fine-grained sparsity from scratch. Specifically, we first make a re-formulation to convert the N:M fine-grained sparsity into a combinatorial problem, in which, the object falls into choosing the best weight combination among $C_M^N$ candidates. Then, we equip each combination with a learnable importance score, which can be jointly optimized along with its associated weights. Through rigorous proof, we demonstrate that the magnitude of the optimized score well reflects the importance of its corresponding weights combination to the training loss. Therefore, by gradually removing combinations with smaller scores till the best one is left, N:M fine-grained sparsity can be efficiently optimized during the normal training phase without any extra expenditure. Comprehensive experimental results have demonstrated that our proposed method for learning best combination, dubbed as LBC, consistently increases the efficacy of the off-the-shelf N:M methods across varying networks and datasets. Our project is released at https://github.com/zyxxmu/LBC. | null | null |
Why Do Artificially Generated Data Help Adversarial Robustness | https://papers.nips.cc/paper_files/paper/2022/hash/065e259a1d2d955e63b99aac6a3a3081-Abstract-Conference.html | Yue Xing, Qifan Song, Guang Cheng | https://papers.nips.cc/paper_files/paper/2022/hash/065e259a1d2d955e63b99aac6a3a3081-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18927-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/065e259a1d2d955e63b99aac6a3a3081-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/065e259a1d2d955e63b99aac6a3a3081-Supplemental-Conference.zip | In the adversarial training framework of \cite{carmon2019unlabeled,gowal2021improving}, people use generated/real unlabeled data with pseudolabels to improve adversarial robustness. We provide statistical insights to explain why the artificially generated data improve adversarial training. In particular, we study how the attack strength and the quality of the unlabeled data affect adversarial robustness in this framework. Our results show that with a high-quality unlabeled data generator, adversarial training can benefit greatly from this framework under large attack strength, while a poor generator can still help to some extent. To make adaptions concerning the quality of generated data, we propose an algorithm that performs online adjustment to the weight between the labeled real data and the generated data, aiming to optimize the adversarial risk. Numerical studies are conducted to verify our theories and show the effectiveness of the proposed algorithm. | null | null |
Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera | https://papers.nips.cc/paper_files/paper/2022/hash/06a52a54c8ee03cd86771136bc91eb1f-Abstract-Conference.html | Hongrui Cai, Wanquan Feng, Xuetao Feng, Yan Wang, Juyong Zhang | https://papers.nips.cc/paper_files/paper/2022/hash/06a52a54c8ee03cd86771136bc91eb1f-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18667-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/06a52a54c8ee03cd86771136bc91eb1f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/06a52a54c8ee03cd86771136bc91eb1f-Supplemental-Conference.zip | We propose Neural-DynamicReconstruction (NDR), a template-free method to recover high-fidelity geometry and motions of a dynamic scene from a monocular RGB-D camera. In NDR, we adopt the neural implicit function for surface representation and rendering such that the captured color and depth can be fully utilized to jointly optimize the surface and deformations. To represent and constrain the non-rigid deformations, we propose a novel neural invertible deforming network such that the cycle consistency between arbitrary two frames is automatically satisfied. Considering that the surface topology of dynamic scene might change over time, we employ a topology-aware strategy to construct the topology-variant correspondence for the fused frames. NDR also further refines the camera poses in a global optimization manner. Experiments on public datasets and our collected dataset demonstrate that NDR outperforms existing monocular dynamic reconstruction methods. | null | null |
Global Optimal K-Medoids Clustering of One Million Samples | https://papers.nips.cc/paper_files/paper/2022/hash/06abed94583030dd50abe6767bd643b1-Abstract-Conference.html | Jiayang Ren, Kaixun Hua, Yankai Cao | https://papers.nips.cc/paper_files/paper/2022/hash/06abed94583030dd50abe6767bd643b1-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18839-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/06abed94583030dd50abe6767bd643b1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/06abed94583030dd50abe6767bd643b1-Supplemental-Conference.pdf | We study the deterministic global optimization of the K-Medoids clustering problem. This work proposes a branch and bound (BB) scheme, in which a tailored Lagrangian relaxation method proposed in the 1970s is used to provide a lower bound at each BB node. The lower bounding method already guarantees the maximum gap at the root node. A closed-form solution to the lower bound can be derived analytically without explicitly solving any optimization problems, and its computation can be easily parallelized. Moreover, with this lower bounding method, finite convergence to the global optimal solution can be guaranteed by branching only on the regions of medoids. We also present several tailored bound tightening techniques to reduce the search space and computational cost. Extensive computational studies on 28 machine learning datasets demonstrate that our algorithm can provide a provable global optimal solution with an optimality gap of 0.1\% within 4 hours on datasets with up to one million samples. Besides, our algorithm can obtain better or equal objective values than the heuristic method. A theoretical proof of global convergence for our algorithm is also presented. | null | null |
Batch Multi-Fidelity Active Learning with Budget Constraints | https://papers.nips.cc/paper_files/paper/2022/hash/06ea400b9b7cfce6428ec27a371632eb-Abstract-Conference.html | Shibo Li, Jeff M Phillips, Xin Yu, Robert Kirby, Shandian Zhe | https://papers.nips.cc/paper_files/paper/2022/hash/06ea400b9b7cfce6428ec27a371632eb-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16874-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/06ea400b9b7cfce6428ec27a371632eb-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/06ea400b9b7cfce6428ec27a371632eb-Supplemental-Conference.pdf | Learning functions with high-dimensional outputs is critical in many applications, such as physical simulation and engineering design. However, collecting training examples for these applications is often costly, e.g., by running numerical solvers. The recent work (Li et al., 2022) proposes the first multi-fidelity active learning approach for high-dimensional outputs, which can acquire examples at different fidelities to reduce the cost while improving the learning performance. However, this method only queries at one pair of fidelity and input at a time, and hence has a risk of bringing in strongly correlated examples to reduce the learning efficiency. In this paper, we propose Batch Multi-Fidelity Active Learning with Budget Constraints (BMFAL-BC), which can promote the diversity of training examples to improve the benefit-cost ratio, while respecting a given budget constraint for batch queries. Hence, our method can be more practically useful. Specifically, we propose a novel batch acquisition function that measures the mutual information between a batch of multi-fidelity queries and the target function, so as to penalize highly correlated queries and encourages diversity. The optimization of the batch acquisition function is challenging in that it involves a combinatorial search over many fidelities while subject to the budget constraint. To address this challenge, we develop a weighted greedy algorithm that can sequentially identify each (fidelity, input) pair, while achieving a near $(1 - 1/e)$-approximation of the optimum. We show the advantage of our method in several computational physics and engineering applications. | null | null |
UniCLIP: Unified Framework for Contrastive Language-Image Pre-training | https://papers.nips.cc/paper_files/paper/2022/hash/072fd0525592b43da661e254bbaadc27-Abstract-Conference.html | Janghyeon Lee, Jongsuk Kim, Hyounguk Shon, Bumsoo Kim, Seung Hwan Kim, Honglak Lee, Junmo Kim | https://papers.nips.cc/paper_files/paper/2022/hash/072fd0525592b43da661e254bbaadc27-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18402-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/072fd0525592b43da661e254bbaadc27-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/072fd0525592b43da661e254bbaadc27-Supplemental-Conference.pdf | Pre-training vision-language models with contrastive objectives has shown promising results that are both scalable to large uncurated datasets and transferable to many downstream applications. Some following works have targeted to improve data efficiency by adding self-supervision terms, but inter-domain (image-text) contrastive loss and intra-domain (image-image) contrastive loss are defined on individual spaces in those works, so many feasible combinations of supervision are overlooked. To overcome this issue, we propose UniCLIP, a Unified framework for Contrastive Language-Image Pre-training. UniCLIP integrates the contrastive loss of both inter-domain pairs and intra-domain pairs into a single universal space. The discrepancies that occur when integrating contrastive loss between different domains are resolved by the three key components of UniCLIP: (1) augmentation-aware feature embedding, (2) MP-NCE loss, and (3) domain dependent similarity measure. UniCLIP outperforms previous vision-language pre-training methods on various single- and multi-modality downstream tasks. In our experiments, we show that each component that comprises UniCLIP contributes well to the final performance. | null | null |
Efficient Multi-agent Communication via Self-supervised Information Aggregation | https://papers.nips.cc/paper_files/paper/2022/hash/075b2875e2b671ddd74aeec0ac9f0357-Abstract-Conference.html | Cong Guan, Feng Chen, Lei Yuan, Chenghe Wang, Hao Yin, Zongzhang Zhang, Yang Yu | https://papers.nips.cc/paper_files/paper/2022/hash/075b2875e2b671ddd74aeec0ac9f0357-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17163-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/075b2875e2b671ddd74aeec0ac9f0357-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/075b2875e2b671ddd74aeec0ac9f0357-Supplemental-Conference.zip | Utilizing messages from teammates can improve coordination in cooperative Multi-agent Reinforcement Learning (MARL). To obtain meaningful information for decision-making, previous works typically combine raw messages generated by teammates with local information as inputs for policy. However, neglecting the aggregation of multiple messages poses great inefficiency for policy learning. Motivated by recent advances in representation learning, we argue that efficient message aggregation is essential for good coordination in MARL. In this paper, we propose Multi-Agent communication via Self-supervised Information Aggregation (MASIA), with which agents can aggregate the received messages into compact representations with high relevance to augment the local policy. Specifically, we design a permutation invariant message encoder to generate common information aggregated representation from raw messages and optimize it via reconstructing and shooting future information in a self-supervised manner. Each agent would utilize the most relevant parts of the aggregated representation for decision-making by a novel message extraction mechanism. Empirical results demonstrate that our method significantly outperforms strong baselines on multiple cooperative MARL tasks for various task settings. | null | null |
Accelerated Training of Physics-Informed Neural Networks (PINNs) using Meshless Discretizations | https://papers.nips.cc/paper_files/paper/2022/hash/0764db1151b936aca59249e2c1386101-Abstract-Conference.html | Ramansh Sharma, Varun Shankar | https://papers.nips.cc/paper_files/paper/2022/hash/0764db1151b936aca59249e2c1386101-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17755-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0764db1151b936aca59249e2c1386101-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0764db1151b936aca59249e2c1386101-Supplemental-Conference.zip | Physics-informed neural networks (PINNs) are neural networks trained by using physical laws in the form of partial differential equations (PDEs) as soft constraints. We present a new technique for the accelerated training of PINNs that combines modern scientific computing techniques with machine learning: discretely-trained PINNs (DT-PINNs). The repeated computation of the partial derivative terms in the PINN loss functions via automatic differentiation during training is known to be computationally expensive, especially for higher-order derivatives. DT-PINNs are trained by replacing these exact spatial derivatives with high-order accurate numerical discretizations computed using meshless radial basis function-finite differences (RBF-FD) and applied via sparse-matrix vector multiplication. While in principle any high-order discretization may be used, the use of RBF-FD allows for DT-PINNs to be trained even on point cloud samples placed on irregular domain geometries. Additionally, though traditional PINNs (vanilla-PINNs) are typically stored and trained in 32-bit floating-point (fp32) on the GPU, we show that for DT-PINNs, using fp64 on the GPU leads to significantly faster training times than fp32 vanilla-PINNs with comparable accuracy. We demonstrate the efficiency and accuracy of DT-PINNs via a series of experiments. First, we explore the effect of network depth on both numerical and automatic differentiation of a neural network with random weights and show that RBF-FD approximations of third-order accuracy and above are more efficient while being sufficiently accurate. We then compare the DT-PINNs to vanilla-PINNs on both linear and nonlinear Poisson equations and show that DT-PINNs achieve similar losses with 2-4x faster training times on a consumer GPU. Finally, we also demonstrate that similar results can be obtained for the PINN solution to the heat equation (a space-time problem) by discretizing the spatial derivatives using RBF-FD and using automatic differentiation for the temporal derivative. Our results show that fp64 DT-PINNs offer a superior cost-accuracy profile to fp32 vanilla-PINNs, opening the door to a new paradigm of leveraging scientific computing techniques to support machine learning. | null | null |
DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning | https://papers.nips.cc/paper_files/paper/2022/hash/076a93fd42aa85f5ccee921a01d77dd5-Abstract-Conference.html | Archana Bura, Aria HasanzadeZonuzy, Dileep Kalathil, Srinivas Shakkottai, Jean-Francois Chamberland | https://papers.nips.cc/paper_files/paper/2022/hash/076a93fd42aa85f5ccee921a01d77dd5-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16817-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/076a93fd42aa85f5ccee921a01d77dd5-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/076a93fd42aa85f5ccee921a01d77dd5-Supplemental-Conference.pdf | Safe reinforcement learning is extremely challenging--not only must the agent explore an unknown environment, it must do so while ensuring no safety constraint violations. We formulate this safe reinforcement learning (RL) problem using the framework of a finite-horizon Constrained Markov Decision Process (CMDP) with an unknown transition probability function, where we model the safety requirements as constraints on the expected cumulative costs that must be satisfied during all episodes of learning. We propose a model-based safe RL algorithm that we call Doubly Optimistic and Pessimistic Exploration (DOPE), and show that it achieves an objective regret $\tilde{O}(|\mathcal{S}|\sqrt{|\mathcal{A}| K})$ without violating the safety constraints during learning, where $|\mathcal{S}|$ is the number of states, $|\mathcal{A}|$ is the number of actions, and $K$ is the number of learning episodes. Our key idea is to combine a reward bonus for exploration (optimism) with a conservative constraint (pessimism), in addition to the standard optimistic model-based exploration. DOPE is not only able to improve the objective regret bound, but also shows a significant empirical performance improvement as compared to earlier optimism-pessimism approaches. | null | null |
Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs | https://papers.nips.cc/paper_files/paper/2022/hash/078fa8f77ce55ef6e9cf79275b88acb0-Abstract-Conference.html | Yeoneung Kim, Insoon Yang, Kwang-Sung Jun | https://papers.nips.cc/paper_files/paper/2022/hash/078fa8f77ce55ef6e9cf79275b88acb0-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17935-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/078fa8f77ce55ef6e9cf79275b88acb0-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/078fa8f77ce55ef6e9cf79275b88acb0-Supplemental-Conference.zip | In online learning problems, exploiting low variance plays an important role in obtaining tight performance guarantees yet is challenging because variances are often not known a priori. Recently, considerable progress has been made by Zhang et al. (2021) where they obtain a variance-adaptive regret bound for linear bandits without knowledge of the variances and a horizon-free regret bound for linear mixture Markov decision processes (MDPs). In this paper, we present novel analyses that improve their regret bounds significantly. For linear bandits, we achieve $\tilde O(\min\{d\sqrt{K}, d^{1.5}\sqrt{\sum_{k=1}^K \sigma_k^2}\} + d^2)$ where $d$ is the dimension of the features, $K$ is the time horizon, and $\sigma_k^2$ is the noise variance at time step $k$, and $\tilde O$ ignores polylogarithmic dependence, which is a factor of $d^3$ improvement. For linear mixture MDPs with the assumption of maximum cumulative reward in an episode being in $[0,1]$, we achieve a horizon-free regret bound of $\tilde O(d \sqrt{K} + d^2)$ where $d$ is the number of base models and $K$ is the number of episodes. This is a factor of $d^{3.5}$ improvement in the leading term and $d^7$ in the lower order term. Our analysis critically relies on a novel peeling-based regret analysis that leverages the elliptical potential `count' lemma. | null | null |
Communication-Efficient Topologies for Decentralized Learning with $O(1)$ Consensus Rate | https://papers.nips.cc/paper_files/paper/2022/hash/0790ef700dd0072f4940abda9b7d0005-Abstract-Conference.html | Zhuoqing Song, Weijian Li, Kexin Jin, Lei Shi, Ming Yan, Wotao Yin, Kun Yuan | https://papers.nips.cc/paper_files/paper/2022/hash/0790ef700dd0072f4940abda9b7d0005-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17245-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0790ef700dd0072f4940abda9b7d0005-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0790ef700dd0072f4940abda9b7d0005-Supplemental-Conference.pdf | Decentralized optimization is an emerging paradigm in distributed learning in which agents achieve network-wide solutions by peer-to-peer communication without the central server. Since communication tends to be slower than computation, when each agent communicates with only a few neighboring agents per iteration, they can complete iterations faster than with more agents or a central server. However, the total number of iterations to reach a network-wide solution is affected by the speed at which the information of the agents is ``mixed'' by communication. We found that popular communication topologies either have large degrees (such as stars and complete graphs) or are ineffective at mixing information (such as rings and grids). To address this problem, we propose a new family of topologies, EquiTopo, which has an (almost) constant degree and network-size-independent consensus rate which is used to measure the mixing efficiency.In the proposed family, EquiStatic has a degree of $\Theta(\ln(n))$, where $n$ is the network size, and a series of time-varying one-peer topologies, EquiDyn, has a constant degree of 1. We generate EquiDyn through a certain random sampling procedure. Both of them achieve $n$-independent consensus rate. We apply them to decentralized SGD and decentralized gradient tracking and obtain faster communication and better convergence, both theoretically and empirically. Our code is implemented through BlueFog and available at https://github.com/kexinjinnn/EquiTopo. | null | null |
Moderate-fitting as a Natural Backdoor Defender for Pre-trained Language Models | https://papers.nips.cc/paper_files/paper/2022/hash/0799492e7be38b66d10ead5e8809616d-Abstract-Conference.html | Biru Zhu, Yujia Qin, Ganqu Cui, Yangyi Chen, Weilin Zhao, Chong Fu, Yangdong Deng, Zhiyuan Liu, Jingang Wang, Wei Wu, Maosong Sun, Ming Gu | https://papers.nips.cc/paper_files/paper/2022/hash/0799492e7be38b66d10ead5e8809616d-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18724-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0799492e7be38b66d10ead5e8809616d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0799492e7be38b66d10ead5e8809616d-Supplemental-Conference.zip | Despite the great success of pre-trained language models (PLMs) in a large set of natural language processing (NLP) tasks, there has been a growing concern about their security in real-world applications. Backdoor attack, which poisons a small number of training samples by inserting backdoor triggers, is a typical threat to security. Trained on the poisoned dataset, a victim model would perform normally on benign samples but predict the attacker-chosen label on samples containing pre-defined triggers. The vulnerability of PLMs under backdoor attacks has been proved with increasing evidence in the literature. In this paper, we present several simple yet effective training strategies that could effectively defend against such attacks. To the best of our knowledge, this is the first work to explore the possibility of backdoor-free adaptation for PLMs. Our motivation is based on the observation that, when trained on the poisoned dataset, the PLM's adaptation follows a strict order of two stages: (1) a moderate-fitting stage, where the model mainly learns the major features corresponding to the original task instead of subsidiary features of backdoor triggers, and (2) an overfitting stage, where both features are learned adequately. Therefore, if we could properly restrict the PLM's adaptation to the moderate-fitting stage, the model would neglect the backdoor triggers but still achieve satisfying performance on the original task. To this end, we design three methods to defend against backdoor attacks by reducing the model capacity, training epochs, and learning rate, respectively. Experimental results demonstrate the effectiveness of our methods in defending against several representative NLP backdoor attacks. We also perform visualization-based analysis to attain a deeper understanding of how the model learns different features, and explore the effect of the poisoning ratio. Finally, we explore whether our methods could defend against backdoor attacks for the pre-trained CV model. The codes are publicly available at https://github.com/thunlp/Moderate-fitting. | null | null |
Dataset Distillation via Factorization | https://papers.nips.cc/paper_files/paper/2022/hash/07bc722f08f096e6ea7ee99349ff0a86-Abstract-Conference.html | Songhua Liu, Kai Wang, Xingyi Yang, Jingwen Ye, Xinchao Wang | https://papers.nips.cc/paper_files/paper/2022/hash/07bc722f08f096e6ea7ee99349ff0a86-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19005-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/07bc722f08f096e6ea7ee99349ff0a86-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/07bc722f08f096e6ea7ee99349ff0a86-Supplemental-Conference.zip | In this paper, we study dataset distillation (DD), from a novel perspective and introduce a \emph{dataset factorization} approach, termed \emph{HaBa}, which is a plug-and-play strategy portable to any existing DD baseline. Unlike conventional DD approaches that aim to produce distilled and representative samples, \emph{HaBa} explores decomposing a dataset into two components: data \emph{Ha}llucination networks and \emph{Ba}ses, where the latter is fed into the former to reconstruct image samples. The flexible combinations between bases and hallucination networks, therefore, equip the distilled data with exponential informativeness gain, which largely increase the representation capability of distilled datasets. To furthermore increase the data efficiency of compression results, we further introduce a pair of adversarial contrastive \xw{constraints} on the resultant hallucination networks and bases, which increase the diversity of generated images and inject more discriminant information into the factorization. Extensive comparisons and experiments demonstrate that our method can yield significant improvement on downstream classification tasks compared with previous state of the arts, while reducing the total number of compressed parameters by up to 65\%. Moreover, distilled datasets by our approach also achieve \textasciitilde10\% higher accuracy than baseline methods in cross-architecture generalization. Our code is available \href{https://github.com/Huage001/DatasetFactorization}{here}. | null | null |
Adaptive Sampling for Discovery | https://papers.nips.cc/paper_files/paper/2022/hash/07bc8125400bf4b140c332010756bd9b-Abstract-Conference.html | Ziping Xu, Eunjae Shim, Ambuj Tewari, Paul Zimmerman | https://papers.nips.cc/paper_files/paper/2022/hash/07bc8125400bf4b140c332010756bd9b-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17042-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/07bc8125400bf4b140c332010756bd9b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/07bc8125400bf4b140c332010756bd9b-Supplemental-Conference.zip | In this paper, we study a sequential decision-making problem, called Adaptive Sampling for Discovery (ASD). Starting with a large unlabeled dataset, algorithms for ASD adaptively label the points with the goal to maximize the sum of responses.This problem has wide applications to real-world discovery problems, for example drug discovery with the help of machine learning models. ASD algorithms face the well-known exploration-exploitation dilemma. The algorithm needs to choose points that yield information to improve model estimates but it also needs to exploit the model. We rigorously formulate the problem and propose a general information-directed sampling (IDS) algorithm. We provide theoretical guarantees for the performance of IDS in linear, graph and low-rank models. The benefits of IDS are shown in both simulation experiments and real-data experiments for discovering chemical reaction conditions. | null | null |
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation | https://papers.nips.cc/paper_files/paper/2022/hash/08050f40fff41616ccfc3080e60a301a-Abstract-Conference.html | Meng-Hao Guo, Cheng-Ze Lu, Qibin Hou, Zhengning Liu, Ming-Ming Cheng, Shi-min Hu | https://papers.nips.cc/paper_files/paper/2022/hash/08050f40fff41616ccfc3080e60a301a-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18721-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/08050f40fff41616ccfc3080e60a301a-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/08050f40fff41616ccfc3080e60a301a-Supplemental-Conference.pdf | We present SegNeXt, a simple convolutional network architecture for semantic segmentation. Recent transformer-based models have dominated the field of se- mantic segmentation due to the efficiency of self-attention in encoding spatial information. In this paper, we show that convolutional attention is a more efficient and effective way to encode contextual information than the self-attention mech- anism in transformers. By re-examining the characteristics owned by successful segmentation models, we discover several key components leading to the perfor- mance improvement of segmentation models. This motivates us to design a novel convolutional attention network that uses cheap convolutional operations. Without bells and whistles, our SegNeXt significantly improves the performance of previous state-of-the-art methods on popular benchmarks, including ADE20K, Cityscapes, COCO-Stuff, Pascal VOC, Pascal Context, and iSAID. Notably, SegNeXt out- performs EfficientNet-L2 w/ NAS-FPN and achieves 90.6% mIoU on the Pascal VOC 2012 test leaderboard using only 1/10 parameters of it. On average, SegNeXt achieves about 2.0% mIoU improvements compared to the state-of-the-art methods on the ADE20K datasets with the same or fewer computations. | null | null |
Understanding Hyperdimensional Computing for Parallel Single-Pass Learning | https://papers.nips.cc/paper_files/paper/2022/hash/080be5eb7e887319ff30c792c2cbc28c-Abstract-Conference.html | Tao Yu, Yichi Zhang, Zhiru Zhang, Christopher M. De Sa | https://papers.nips.cc/paper_files/paper/2022/hash/080be5eb7e887319ff30c792c2cbc28c-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17895-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/080be5eb7e887319ff30c792c2cbc28c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/080be5eb7e887319ff30c792c2cbc28c-Supplemental-Conference.pdf | Hyperdimensional computing (HDC) is an emerging learning paradigm that computes with high dimensional binary vectors. There is an active line of research on HDC in the community of emerging hardware because of its energy efficiency and ultra-low latency---but HDC suffers from low model accuracy, with little theoretical understanding of what limits its performance. We propose a new theoretical analysis of the limits of HDC via a consideration of what similarity matrices can be expressed'' by binary vectors, and we show how the limits of HDC can be approached using random Fourier features (RFF). We extend our analysis to the more general class of vector symbolic architectures (VSA), which compute with high-dimensional vectors (hypervectors) that are not necessarily binary. We propose a new class of VSAs, finite group VSAs, which surpass the limits of HDC. Using representation theory, we characterize which similarity matrices can beexpressed'' by finite group VSA hypervectors, and we show how these VSAs can be constructed. Experimental results show that our RFF method and group VSA can both outperform the state-of-the-art HDC model by up to 7.6\% while maintaining hardware efficiency. This work aims to inspire a future interest on HDC in the ML community and connect to the hardware community. | null | null |
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms | https://papers.nips.cc/paper_files/paper/2022/hash/082e82cae0232f45f27fdd2612c31f8a-Abstract-Conference.html | QIN DING, Yue Kang, Yi-Wei Liu, Thomas Chun Man Lee, Cho-Jui Hsieh, James Sharpnack | https://papers.nips.cc/paper_files/paper/2022/hash/082e82cae0232f45f27fdd2612c31f8a-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17454-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/082e82cae0232f45f27fdd2612c31f8a-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/082e82cae0232f45f27fdd2612c31f8a-Supplemental-Conference.zip | The stochastic contextual bandit problem, which models the trade-off between exploration and exploitation, has many real applications, including recommender systems, online advertising and clinical trials. As many other machine learning algorithms, contextual bandit algorithms often have one or more hyper-parameters. As an example, in most optimal stochastic contextual bandit algorithms, there is an unknown exploration parameter which controls the trade-off between exploration and exploitation. A proper choice of the hyper-parameters is essential for contextual bandit algorithms to perform well. However, it is infeasible to use offline tuning methods to select hyper-parameters in contextual bandit environment since there is no pre-collected dataset and the decisions have to be made in real time. To tackle this problem, we first propose a two-layer bandit structure for auto tuning the exploration parameter and further generalize it to the Syndicated Bandits framework which can learn multiple hyper-parameters dynamically in contextual bandit environment. We derive the regret bounds of our proposed Syndicated Bandits framework and show it can avoid its regret dependent exponentially in the number of hyper-parameters to be tuned. Moreover, it achieves optimal regret bounds under certain scenarios. Syndicated Bandits framework is general enough to handle the tuning tasks in many popular contextual bandit algorithms, such as LinUCB, LinTS, UCB-GLM, etc. Experiments on both synthetic and real datasets validate the effectiveness of our proposed framework. | null | null |
Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting | https://papers.nips.cc/paper_files/paper/2022/hash/08342dc6ab69f23167b4123086ad4d38-Abstract-Conference.html | Neil Mallinar, James Simon, Amirhesam Abedsoltan, Parthe Pandit, Misha Belkin, Preetum Nakkiran | https://papers.nips.cc/paper_files/paper/2022/hash/08342dc6ab69f23167b4123086ad4d38-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18289-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/08342dc6ab69f23167b4123086ad4d38-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/08342dc6ab69f23167b4123086ad4d38-Supplemental-Conference.zip | The practical success of overparameterized neural networks has motivated the recent scientific study of \emph{interpolating methods}-- learning methods which are able fit their training data perfectly. Empirically, certain interpolating methods can fit noisy training data without catastrophically bad test performance, which defies standard intuitions from statistical learning theory. Aiming to explain this, a large body of recent work has studied \emph{benign overfitting}, a behavior seen in certain asymptotic settings under which interpolating methods approach Bayes-optimality, even in the presence of noise. In this work, we argue that, while benign overfitting has been instructive to study, real interpolating methods like deep networks do not fit benignly. That is, noise in the train set leads to suboptimal generalization, suggesting that these methods fall in an intermediate regime between benign and catastrophic overfitting, in which asymptotic risk is neither is neither Bayes-optimal nor unbounded, with the confounding effect of the noise being ``tempered" but non-negligible. We call this behavior \textit{tempered overfitting}. We first provide broad empirical evidence for our three-part taxonomy, demonstrating that deep neural networks and kernel machines fit to noisy data can be reasonably well classified as benign, tempered, or catastrophic. We then specialize to kernel (ridge) regression (KR), obtaining conditions on the ridge parameter and kernel eigenspectrum under which KR exhibits each of the three behaviors, demonstrating the consequences for KR with common kernels and trained neural networks of infinite width using experiments on natural and synthetic datasets. | null | null |
Pre-trained Adversarial Perturbations | https://papers.nips.cc/paper_files/paper/2022/hash/084727e8abf90a8365b940036329cb6f-Abstract-Conference.html | Yuanhao Ban, Yinpeng Dong | https://papers.nips.cc/paper_files/paper/2022/hash/084727e8abf90a8365b940036329cb6f-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19219-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/084727e8abf90a8365b940036329cb6f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/084727e8abf90a8365b940036329cb6f-Supplemental-Conference.pdf | Self-supervised pre-training has drawn increasing attention in recent years due to its superior performance on numerous downstream tasks after fine-tuning. However, it is well-known that deep learning models lack the robustness to adversarial examples, which can also invoke security issues to pre-trained models, despite being less explored. In this paper, we delve into the robustness of pre-trained models by introducing Pre-trained Adversarial Perturbations (PAPs), which are universal perturbations crafted for the pre-trained models to maintain the effectiveness when attacking fine-tuned ones without any knowledge of the downstream tasks. To this end, we propose a Low-Level Layer Lifting Attack (L4A) method to generate effective PAPs by lifting the neuron activations of low-level layers of the pre-trained models. Equipped with an enhanced noise augmentation strategy, L4A is effective at generating more transferable PAPs against the fine-tuned models. Extensive experiments on typical pre-trained vision models and ten downstream tasks demonstrate that our method improves the attack success rate by a large margin compared to the state-of-the-art methods. | null | null |
An Empirical Study on Disentanglement of Negative-free Contrastive Learning | https://papers.nips.cc/paper_files/paper/2022/hash/0850e04a62e0f3407780852581c5fcf4-Abstract-Conference.html | Jinkun Cao, Ruiqian Nai, Qing Yang, Jialei Huang, Yang Gao | https://papers.nips.cc/paper_files/paper/2022/hash/0850e04a62e0f3407780852581c5fcf4-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17595-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0850e04a62e0f3407780852581c5fcf4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0850e04a62e0f3407780852581c5fcf4-Supplemental-Conference.zip | Negative-free contrastive learning methods have attracted a lot of attention with simplicity and impressive performances for large-scale pretraining. However, its disentanglement property remains unexplored. In this paper, we examine negative-free contrastive learning methods to study the disentanglement property empirically. We find that existing disentanglement metrics fail to make meaningful measurements for high-dimensional representation models, so we propose a new disentanglement metric based on Mutual Information between latent representations and data factors. With this proposed metric, we benchmark the disentanglement property of negative-free contrastive learning on both popular synthetic datasets and a real-world dataset CelebA. Our study shows that the investigated methods can learn a well-disentangled subset of representation. As far as we know, we are the first to extend the study of disentangled representation learning to high-dimensional representation space and introduce negative-free contrastive learning methods into this area. The source code of this paper is available at https://github.com/noahcao/disentanglementlibmed. | null | null |
MABSplit: Faster Forest Training Using Multi-Armed Bandits | https://papers.nips.cc/paper_files/paper/2022/hash/08857467641ad82f635023d530605b4c-Abstract-Conference.html | Mo Tiwari, Ryan Kang, Jaeyong Lee, Chris Piech, Ilan Shomorony, Sebastian Thrun, Martin J. Zhang | https://papers.nips.cc/paper_files/paper/2022/hash/08857467641ad82f635023d530605b4c-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16915-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/08857467641ad82f635023d530605b4c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/08857467641ad82f635023d530605b4c-Supplemental-Conference.pdf | Random forests are some of the most widely used machine learning models today, especially in domains that necessitate interpretability. We present an algorithm that accelerates the training of random forests and other popular tree-based learning methods. At the core of our algorithm is a novel node-splitting subroutine, dubbed MABSplit, used to efficiently find split points when constructing decision trees. Our algorithm borrows techniques from the multi-armed bandit literature to judiciously determine how to allocate samples and computational power across candidate split points. We provide theoretical guarantees that MABSplit improves the sample complexity of each node split from linear to logarithmic in the number of data points. In some settings, MABSplit leads to 100x faster training (an 99% reduction in training time) without any decrease in generalization performance. We demonstrate similar speedups when MABSplit is used across a variety of forest-based variants, such as Extremely Random Forests and Random Patches. We also show our algorithm can be used in both classification and regression tasks. Finally, we show that MABSplit outperforms existing methods in generalization performance and feature importance calculations under a fixed computational budget. All of our experimental results are reproducible via a one-line script at https://github.com/ThrunGroup/FastForest. | null | null |
Counterfactual Fairness with Partially Known Causal Graph | https://papers.nips.cc/paper_files/paper/2022/hash/08887999616116910fccec17a63584b5-Abstract-Conference.html | Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong | https://papers.nips.cc/paper_files/paper/2022/hash/08887999616116910fccec17a63584b5-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17089-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/08887999616116910fccec17a63584b5-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/08887999616116910fccec17a63584b5-Supplemental-Conference.pdf | Fair machine learning aims to avoid treating individuals or sub-populations unfavourably based on \textit{sensitive attributes}, such as gender and race. Those methods in fair machine learning that are built on causal inference ascertain discrimination and bias through causal effects. Though causality-based fair learning is attracting increasing attention, current methods assume the true causal graph is fully known. This paper proposes a general method to achieve the notion of counterfactual fairness when the true causal graph is unknown. To select features that lead to counterfactual fairness, we derive the conditions and algorithms to identify ancestral relations between variables on a \textit{Partially Directed Acyclic Graph (PDAG)}, specifically, a class of causal DAGs that can be learned from observational data combined with domain knowledge. Interestingly, we find that counterfactual fairness can be achieved as if the true causal graph were fully known, when specific background knowledge is provided: the sensitive attributes do not have ancestors in the causal graph. Results on both simulated and real-world datasets demonstrate the effectiveness of our method. | null | null |
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints | https://papers.nips.cc/paper_files/paper/2022/hash/089b592cccfafdca8e0178e85b609f19-Abstract-Conference.html | Jose Gallego-Posada, Juan Ramirez, Akram Erraqabi, Yoshua Bengio, Simon Lacoste-Julien | https://papers.nips.cc/paper_files/paper/2022/hash/089b592cccfafdca8e0178e85b609f19-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17807-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/089b592cccfafdca8e0178e85b609f19-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/089b592cccfafdca8e0178e85b609f19-Supplemental-Conference.pdf | The performance of trained neural networks is robust to harsh levels of pruning. Coupled with the ever-growing size of deep learning models, this observation has motivated extensive research on learning sparse models. In this work, we focus on the task of controlling the level of sparsity when performing sparse learning. Existing methods based on sparsity-inducing penalties involve expensive trial-and-error tuning of the penalty factor, thus lacking direct control of the resulting model sparsity. In response, we adopt a constrained formulation: using the gate mechanism proposed by Louizos et al. (2018), we formulate a constrained optimization problem where sparsification is guided by the training objective and the desired sparsity target in an end-to-end fashion. Experiments on CIFAR-{10, 100}, TinyImageNet, and ImageNet using WideResNet and ResNet{18, 50} models validate the effectiveness of our proposal and demonstrate that we can reliably achieve pre-determined sparsity targets without compromising on predictive performance. | null | null |
Algorithms and Hardness for Learning Linear Thresholds from Label Proportions | https://papers.nips.cc/paper_files/paper/2022/hash/08a9e28c96d016dd63903ab51cd085b0-Abstract-Conference.html | Rishi Saket | https://papers.nips.cc/paper_files/paper/2022/hash/08a9e28c96d016dd63903ab51cd085b0-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17810-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/08a9e28c96d016dd63903ab51cd085b0-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/08a9e28c96d016dd63903ab51cd085b0-Supplemental-Conference.pdf | We study the learnability of linear threshold functions (LTFs) in the learning from label proportions (LLP) framework. In this, the feature-vector classifier is learnt from bags of feature-vectors and their corresponding observed label proportions which are satisfied by (i.e., consistent with) some unknown LTF. This problem has been investigated in recent work (Saket21) which gave an algorithm to produce an LTF that satisfies at least $(2/5)$-fraction of a satisfiable collection of bags, each of size $\leq 2$, by solving and rounding a natural SDP relaxation. However, this SDP relaxation is specific to at most $2$-sized bags and does not apply to bags of larger size. In this work we provide a fairly non-trivial SDP relaxation of a non-quadratic formulation for bags of size $3$. We analyze its rounding procedure using novel matrix decomposition techniques to obtain an algorithm which outputs an LTF satisfying at least $(1/12)$-fraction of the bags of size $\leq 3$. We also apply our techniques to bags of size $q \geq 4$ to provide a $\Omega\left(1/q\right)$-approximation guarantee for a weaker notion of satisfiability. We include comparative experiments on simulated data demonstrating the applicability of our algorithmic techniques. From the complexity side we provide a hardness reduction to produce instances with bags of any constant size $q$. Our reduction proves the NP-hardness of satisfying more than $({1}/{q}) + o(1)$ fraction of a satisfiable collection of such bags using as hypothesis any function of constantly many LTFs, showing thereby that the problem is harder to approximate as the bag size $q$ increases. Using a strengthened analysis, for $q=2$ we obtain a $({4}/{9}) +o(1)$ hardness factor for this problem, improving upon the $({1}/{2}) + o(1)$ factor shown by Saket21. | null | null |
Predictive Coding beyond Gaussian Distributions | https://papers.nips.cc/paper_files/paper/2022/hash/08f9de0232c0b485110237f6e6cf88f1-Abstract-Conference.html | Luca Pinchetti, Tommaso Salvatori, Yordan Yordanov, Beren Millidge, Yuhang Song, Thomas Lukasiewicz | https://papers.nips.cc/paper_files/paper/2022/hash/08f9de0232c0b485110237f6e6cf88f1-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17495-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/08f9de0232c0b485110237f6e6cf88f1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/08f9de0232c0b485110237f6e6cf88f1-Supplemental-Conference.pdf | A large amount of recent research has the far-reaching goal of finding training methods for deep neural networks that can serve as alternatives to backpropagation~(BP). A prominent example is predictive coding (PC), which is a neuroscience-inspired method that performs inference on hierarchical Gaussian generative models. These methods, however, fail to keep up with modern neural networks, as they are unable to replicate the dynamics of complex layers and activation functions. In this work, we solve this problem by generalizing PC to arbitrary probability distributions, enabling the training of architectures, such as transformers, that are hard to approximate with only Gaussian assumptions. We perform three experimental analyses. First, we study the gap between our method and the standard formulation of PC on multiple toy examples. Second, we test the reconstruction quality on variational autoencoders, where our method reaches the same reconstruction quality as BP. Third, we show that our method allows us to train transformer networks and achieve performance comparable with BP on conditional language models. More broadly, this method allows neuroscience-inspired learning to be applied to multiple domains, since the internal distributions can be flexibly adapted to the data, tasks, and architectures used. | null | null |
Semi-supervised Active Linear Regression | https://papers.nips.cc/paper_files/paper/2022/hash/08fe4b20d554296e503f5a43795c78d6-Abstract-Conference.html | Nived Rajaraman, Fnu Devvrit, Pranjal Awasthi | https://papers.nips.cc/paper_files/paper/2022/hash/08fe4b20d554296e503f5a43795c78d6-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16896-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/08fe4b20d554296e503f5a43795c78d6-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/08fe4b20d554296e503f5a43795c78d6-Supplemental-Conference.pdf | Labeled data often comes at a high cost as it may require recruiting human labelers or running costly experiments. At the same time, in many practical scenarios, one already has access to a partially labeled, potentially biased dataset that can help with the learning task at hand. Motivated by such settings, we formally initiate a study of ``semi-supervised active learning'' through the frame of linear regression. Here, the learner has access to a dataset $X \in \mathbb{R}^{(n_{\text{un}}+n_{\text{lab}}) \times d}$ composed of $n_{\text{un}}$ unlabeled examples that a learner can actively query, and $n_{\text{lab}}$ examples labeled a priori. Denoting the true labels by $Y \in \mathbb{R}^{n_{\text{un}} + n_{\text{lab}}}$, the learner's objective is to find $\widehat{\beta} \in \mathbb{R}^d$ such that,$$\| X \widehat{\beta} - Y \|_2^2 \le (1 + \epsilon) \min_{\beta \in \mathbb{R}^d} \| X \beta - Y \|_2^2$$while querying the labels of as few unlabeled points as possible. In this paper, we introduce an instance dependent parameter called the reduced rank, denoted $\text{R}_X$, and propose an efficient algorithm with query complexity $O(\text{R}_X/\epsilon)$. This result directly implies improved upper bounds for two important special cases: $(i)$ active ridge regression, and $(ii)$ active kernel ridge regression, where the reduced-rank equates to the ``statistical dimension'', $\textsf{sd}_\lambda$ and ``effective dimension'', $d_\lambda$ of the problem respectively, where $\lambda \ge 0$ denotes the regularization parameter. Finally, we introduce a distributional version of the problem as a special case of the agnostic formulation we consider earlier; here, for every $X$, we prove a matching instance-wise lower bound of $\Omega (\text{R}_X / \epsilon)$ on the query complexity of any algorithm. | null | null |
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness | https://papers.nips.cc/paper_files/paper/2022/hash/08fe50bf209c57eecf0804f9f9ed639f-Abstract-Conference.html | Avrim Blum, Omar Montasser, Greg Shakhnarovich, Hongyang Zhang | https://papers.nips.cc/paper_files/paper/2022/hash/08fe50bf209c57eecf0804f9f9ed639f-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18884-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/08fe50bf209c57eecf0804f9f9ed639f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/08fe50bf209c57eecf0804f9f9ed639f-Supplemental-Conference.pdf | We present an oracle-efficient algorithm for boosting the adversarial robustness of barely robust learners. Barely robust learning algorithms learn predictors that are adversarially robust only on a small fraction $\beta \ll 1$ of the data distribution. Our proposed notion of barely robust learning requires robustness with respect to a ``larger'' perturbation set; which we show is necessary for strongly robust learning, and that weaker relaxations are not sufficient for strongly robust learning. Our results reveal a qualitative and quantitative equivalence between two seemingly unrelated problems: strongly robust learning and barely robust learning. | null | null |
Decision-Focused Learning without Decision-Making: Learning Locally Optimized Decision Losses | https://papers.nips.cc/paper_files/paper/2022/hash/0904c7edde20d7134a77fc7f9cd86ea2-Abstract-Conference.html | Sanket Shah, Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe | https://papers.nips.cc/paper_files/paper/2022/hash/0904c7edde20d7134a77fc7f9cd86ea2-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17149-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0904c7edde20d7134a77fc7f9cd86ea2-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0904c7edde20d7134a77fc7f9cd86ea2-Supplemental-Conference.pdf | Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive model to a downstream optimization task that uses its predictions in order to perform better \textit{on that specific task}. The main technical challenge associated with DFL is that it requires being able to differentiate through the optimization problem, which is difficult due to discontinuous solutions and other challenges. Past work has largely gotten around this this issue by \textit{handcrafting} task-specific surrogates to the original optimization problem that provide informative gradients when differentiated through. However, the need to handcraft surrogates for each new task limits the usability of DFL. In addition, there are often no guarantees about the convexity of the resulting surrogates and, as a result, training a predictive model using them can lead to inferior local optima. In this paper, we do away with surrogates altogether and instead \textit{learn} loss functions that capture task-specific information. To the best of our knowledge, ours is the first approach that entirely replaces the optimization component of decision-focused learning with a loss that is automatically learned. Our approach (a) only requires access to a black-box oracle that can solve the optimization problem and is thus \textit{generalizable}, and (b) can be \textit{convex by construction} and so can be easily optimized over. We evaluate our approach on three resource allocation problems from the literature and find that our approach outperforms learning without taking into account task-structure in all three domains, and even hand-crafted surrogates from the literature. | null | null |
Neural Stochastic PDEs: Resolution-Invariant Learning of Continuous Spatiotemporal Dynamics | https://papers.nips.cc/paper_files/paper/2022/hash/091166620a04a289c555f411d8899049-Abstract-Conference.html | Cristopher Salvi, Maud Lemercier, Andris Gerasimovics | https://papers.nips.cc/paper_files/paper/2022/hash/091166620a04a289c555f411d8899049-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17640-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/091166620a04a289c555f411d8899049-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/091166620a04a289c555f411d8899049-Supplemental-Conference.pdf | Stochastic partial differential equations (SPDEs) are the mathematical tool of choice for modelling spatiotemporal PDE-dynamics under the influence of randomness. Based on the notion of mild solution of an SPDE, we introduce a novel neural architecture to learn solution operators of PDEs with (possibly stochastic) forcing from partially observed data. The proposed Neural SPDE model provides an extension to two popular classes of physics-inspired architectures. On the one hand, it extends Neural CDEs and variants -- continuous-time analogues of RNNs -- in that it is capable of processing incoming sequential information arriving at arbitrary spatial resolutions. On the other hand, it extends Neural Operators -- generalizations of neural networks to model mappings between spaces of functions -- in that it can parameterize solution operators of SPDEs depending simultaneously on the initial condition and a realization of the driving noise. By performing operations in the spectral domain, we show how a Neural SPDE can be evaluated in two ways, either by calling an ODE solver (emulating a spectral Galerkin scheme), or by solving a fixed point problem. Experiments on various semilinear SPDEs, including the stochastic Navier-Stokes equations, demonstrate how the Neural SPDE model is capable of learning complex spatiotemporal dynamics in a resolution-invariant way, with better accuracy and lighter training data requirements compared to alternative models, and up to 3 orders of magnitude faster than traditional solvers. | null | null |
Okapi: Generalising Better by Making Statistical Matches Match | https://papers.nips.cc/paper_files/paper/2022/hash/0918183ced31affb7ce0345e45ac1943-Abstract-Conference.html | Myles Bartlett, Sara Romiti, Viktoriia Sharmanska, Novi Quadrianto | https://papers.nips.cc/paper_files/paper/2022/hash/0918183ced31affb7ce0345e45ac1943-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17728-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0918183ced31affb7ce0345e45ac1943-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0918183ced31affb7ce0345e45ac1943-Supplemental-Conference.pdf | We propose Okapi, a simple, efficient, and general method for robust semi-supervised learning based on online statistical matching. Our method uses a nearest-neighbours-based matching procedure to generate cross-domain views for a consistency loss, while eliminating statistical outliers. In order to perform the online matching in a runtime- and memory-efficient way, we draw upon the self-supervised literature and combine a memory bank with a slow-moving momentum encoder. The consistency loss is applied within the feature space, rather than on the predictive distribution, making the method agnostic to both the modality and the task in question. We experiment on the WILDS 2.0 datasets Sagawa et al., which significantly expands the range of modalities, applications, and shifts available for studying and benchmarking real-world unsupervised adaptation. Contrary to Sagawa et al., we show that it is in fact possible to leverage additional unlabelled data to improve upon empirical risk minimisation (ERM) results with the right method. Our method outperforms the baseline methods in terms of out-of-distribution (OOD) generalisation on the iWildCam (a multi-class classification task) and PovertyMap (a regression task) image datasets as well as the CivilComments (a binary classification task) text dataset. Furthermore, from a qualitative perspective, we show the matches obtained from the learned encoder are strongly semantically related. Code for our paper is publicly available at https://github.com/wearepal/okapi/. | null | null |
Revisiting Heterophily For Graph Neural Networks | https://papers.nips.cc/paper_files/paper/2022/hash/092359ce5cf60a80e882378944bf1be4-Abstract-Conference.html | Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, Doina Precup | https://papers.nips.cc/paper_files/paper/2022/hash/092359ce5cf60a80e882378944bf1be4-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19041-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/092359ce5cf60a80e882378944bf1be4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/092359ce5cf60a80e882378944bf1be4-Supplemental-Conference.pdf | Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using graph structures based on the relational inductive bias (homophily assumption). While GNNs have been commonly believed to outperform NNs in real-world tasks, recent work has identified a non-trivial set of datasets where their performance compared to NNs is not satisfactory. Heterophily has been considered the main cause of this empirical observation and numerous works have been put forward to address it. In this paper, we first revisit the widely used homophily metrics and point out that their consideration of only graph-label consistency is a shortcoming. Then, we study heterophily from the perspective of post-aggregation node similarity and define new homophily metrics, which are potentially advantageous compared to existing ones. Based on this investigation, we prove that some harmful cases of heterophily can be effectively addressed by local diversification operation. Then, we propose the Adaptive Channel Mixing (ACM), a framework to adaptively exploit aggregation, diversification and identity channels to extract richer localized information in each baseline GNN layer. ACM is more powerful than the commonly used uni-channel framework for node classification tasks on heterophilic graphs. When evaluated on 10 benchmark node classification tasks, ACM-augmented baselines consistently achieve significant performance gain, exceeding state-of-the-art GNNs on most tasks without incurring significant computational burden. (Code: https://github.com/SitaoLuan/ACM-GNN) | null | null |
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NeurIPS 2022 Accepted Paper Meta Info Dataset
This dataset is collected from the NeurIPS 2022 Advances in Neural Information Processing Systems 35 conference accepted paper (https://papers.nips.cc/paper_files/paper/2023) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/nips2022). For researchers who are interested in doing analysis of NIPS 2022 accepted papers and potential research trends, you can use the already cleaned up json file in the dataset. Each row contains the meta information of a paper in the NIPS 2022 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain.
Meta Information of Json File
{
"title": "Federated Submodel Optimization for Hot and Cold Data Features",
"url": "https://papers.nips.cc/paper_files/paper/2022/hash/002262941c9edfd472a79298b2ac5e17-Abstract-Conference.html",
"authors": "Yucheng Ding, Chaoyue Niu, Fan Wu, Shaojie Tang, Chengfei Lyu, yanghe feng, Guihai Chen",
"detail_url": "https://papers.nips.cc/paper_files/paper/2022/hash/002262941c9edfd472a79298b2ac5e17-Abstract-Conference.html",
"tags": "NIPS 2022",
"Bibtex": "https://papers.nips.cc/paper_files/paper/17527-/bibtex",
"Paper": "https://papers.nips.cc/paper_files/paper/2022/file/002262941c9edfd472a79298b2ac5e17-Paper-Conference.pdf",
"Supplemental": "https://papers.nips.cc/paper_files/paper/2022/file/002262941c9edfd472a79298b2ac5e17-Supplemental-Conference.pdf",
"abstract": "We focus on federated learning in practical recommender systems and natural language processing scenarios. The global model for federated optimization typically contains a large and sparse embedding layer, while each client\u2019s local data tend to interact with part of features, updating only a small submodel with the feature-related embedding vectors. We identify a new and important issue that distinct data features normally involve different numbers of clients, generating the differentiation of hot and cold features. We further reveal that the classical federated averaging algorithm (FedAvg) or its variants, which randomly selects clients to participate and uniformly averages their submodel updates, will be severely slowed down, because different parameters of the global model are optimized at different speeds. More specifically, the model parameters related to hot (resp., cold) features will be updated quickly (resp., slowly). We thus propose federated submodel averaging (FedSubAvg), which introduces the number of feature-related clients as the metric of feature heat to correct the aggregation of submodel updates. We prove that due to the dispersion of feature heat, the global objective is ill-conditioned, and FedSubAvg works as a suitable diagonal preconditioner. We also rigorously analyze FedSubAvg\u2019s convergence rate to stationary points. We finally evaluate FedSubAvg over several public and industrial datasets. The evaluation results demonstrate that FedSubAvg significantly outperforms FedAvg and its variants."
}
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