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Unsupervised Learning for Combinatorial Optimization Needs Meta Learning | https://openreview.net/forum?id=-ENYHCE8zBp | https://openreview.net/forum?id=-ENYHCE8zBp | Haoyu Peter Wang,Pan Li | ICLR 2023,Poster | A general framework of unsupervised learning for combinatorial optimization (CO) is to train a neural network whose output gives a problem solution by directly optimizing the CO objective. Albeit with some advantages over traditional solvers, current frameworks optimize an averaged performance over the distribution of historical problem instances, which misaligns with the actual goal of CO that looks for a good solution to every future encountered instance. With this observation, we propose a new objective of unsupervised learning for CO where the goal of learning is to search for good initialization for future problem instances rather than give direct solutions. We propose a meta-learning-based training pipeline for this new objective. Our method achieves good performance. We observe that even the initial solution given by our model before fine-tuning can significantly outperform the baselines under various evaluation settings including evaluation across multiple datasets, and the case with big shifts in the problem scale. The reason we conjecture is that meta-learning-based training lets the model be loosely tied to each local optimum for a training instance while being more adaptive to the changes of optimization landscapes across instances. | https://openreview.net/pdf/e6cfc858d7dfca232e84231fb4af2c1decd454a2.pdf |
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models | https://openreview.net/forum?id=r0BrY4BiEXO | https://openreview.net/forum?id=r0BrY4BiEXO | Liam H Fowl,Jonas Geiping,Steven Reich,Yuxin Wen,Wojciech Czaja,Micah Goldblum,Tom Goldstein | ICLR 2023,Poster | Privacy is a central tenet of Federated learning (FL), in which a central server trains models without centralizing user data. However, gradient updates used in FL can leak user information. While the most industrial uses of FL are for text applications (e.g. keystroke prediction), the majority of attacks on user privacy in FL have focused on simple image classifiers and threat models that assume honest execution of the FL protocol from the server. We propose a novel attack that reveals private user text by deploying malicious parameter vectors, and which succeeds even with mini-batches, multiple users, and long sequences. Unlike previous attacks on FL, the attack exploits characteristics of both the Transformer architecture and the token embedding, separately extracting tokens and positional embeddings to retrieve high-fidelity text. We argue that the threat model of malicious server states is highly relevant from a user-centric perspective, and show that in this scenario, text applications using transformer models are much more vulnerable than previously thought. | https://openreview.net/pdf/b2cbf4d1dffc2268fe28070b8b6a4e40118f959c.pdf |
Adaptive Optimization in the $\infty$-Width Limit | https://openreview.net/forum?id=zgVDqw9ZUES | https://openreview.net/forum?id=zgVDqw9ZUES | Etai Littwin,Greg Yang | ICLR 2023,Poster | Recent works have developed detailed understanding of large neural networks' behaviors via their infinite-width limits, e.g., the neural tangent kernel (NTK) and the feature learning ($\mu$) limits. These theories were developed for stochastic gradient descent. Yet, in practice, all large NN are trained using Adam or other adaptive gradient optimizers (AGO), which are not covered by such previous works. Here, we close this gap via the Tensor Programs framework. Specifically, for deep MLPs, we derive the NTK and $\mu$ parametrizations as well as their infinite-width limits. We find 1) The NTK limit of AGO, in contrast to that of SGD, now depends nonlinearly on the loss derivative but nevertheless still fails to learn features; 2) this is fixed by the $\mu$ limit of AGO (as in the case of SGD). To obtain these results, we extend the Tensor Programs language with a new instruction that allows one to express the gradient processing done by AGOs. | https://openreview.net/pdf/ca33ac2b130128faa1c047e841cc1b64019486a2.pdf |
Broken Neural Scaling Laws | https://openreview.net/forum?id=sckjveqlCZ | https://openreview.net/forum?id=sckjveqlCZ | Ethan Caballero,Kshitij Gupta,Irina Rish,David Krueger | ICLR 2023,Poster | We present a smoothly broken power law functional form (referred to by us as a broken neural scaling law (BNSL)) that accurately models and extrapolates the scaling behaviors of deep neural networks (i.e. how the evaluation metric of interest varies as the amount of compute used for training, number of model parameters, training dataset size, or upstream performance varies) for various architectures and for each of various tasks within a large and diverse set of upstream and downstream tasks, in zero-shot, prompted, and fine-tuned settings. This set includes large-scale vision, language, audio, video, diffusion, generative modeling, multimodal learning, contrastive learning, AI alignment, robotics, out-of-distribution (OOD) generalization, continual learning, uncertainty estimation / calibration, out-of-distribution detection, adversarial robustness, molecules, computer programming/coding, math word problems, arithmetic, unsupervised/self-supervised learning, and reinforcement learning (single agent and multi-agent). When compared to other functional forms for neural scaling behavior, this functional form yields extrapolations of scaling behavior that are considerably more accurate on this set. Moreover, this functional form accurately models and extrapolates scaling behavior that other functional forms are incapable of expressing such as the non-monotonic transitions present in the scaling behavior of phenomena such as double descent and the delayed, sharp inflection points present in the scaling behavior of tasks such as arithmetic. Lastly, we use this functional form to glean insights about the limit of the predictability of scaling behavior. See arXiv for longer version of this paper. Code is available at https://github.com/ethancaballero/broken_neural_scaling_laws | https://openreview.net/pdf/b9c49ee6a7c5cbe69e7836796acf1b820da86174.pdf |
Avoiding spurious correlations via logit correction | https://openreview.net/forum?id=5BaqCFVh5qL | https://openreview.net/forum?id=5BaqCFVh5qL | Sheng Liu,Xu Zhang,Nitesh Sekhar,Yue Wu,Prateek Singhal,Carlos Fernandez-Granda | ICLR 2023,Poster | Empirical studies suggest that machine learning models trained with empirical risk minimization (ERM) often rely on attributes that may be spuriously correlated with the class labels. Such models typically lead to poor performance during inference for data lacking such correlations. In this work, we explicitly consider a situation where potential spurious correlations are present in the majority of training data. In contrast with existing approaches, which use the ERM model outputs to detect the samples without spurious correlations and either heuristically upweight or upsample those samples, we propose the logit correction (LC) loss, a simple yet effective improvement on the softmax cross-entropy loss, to correct the sample logit. We demonstrate that minimizing the LC loss is equivalent to maximizing the group-balanced accuracy, so the proposed LC could mitigate the negative impacts of spurious correlations. Our extensive experimental results further reveal that the proposed LC loss outperforms state-of-the-art solutions on multiple popular benchmarks by a large margin, an average 5.5% absolute improvement, without access to spurious attribute labels. LC is also competitive with oracle methods that make use of the attribute labels. | https://openreview.net/pdf/095952d9a51005ef570d00c32d3a58989450f58f.pdf |
Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-Free RL | https://openreview.net/forum?id=wNUgn1n6esQ | https://openreview.net/forum?id=wNUgn1n6esQ | Ruiquan Huang,Jing Yang,Yingbin Liang | ICLR 2023,Poster | Reward-free reinforcement learning (RF-RL), a recently introduced RL paradigm, relies on random action-taking to explore the unknown environment without any reward feedback information. While the primary goal of the exploration phase in RF-RL is to reduce the uncertainty in the estimated model with minimum number of trajectories, in practice, the agent often needs to abide by certain safety constraint at the same time. It remains unclear how such safe exploration requirement would affect the corresponding sample complexity in order to achieve the desired optimality of the obtained policy in planning. In this work, we make a first attempt to answer this question. In particular, we consider the scenario where a safe baseline policy is known beforehand, and propose a unified Safe reWard-frEe ExploraTion (SWEET) framework. We then particularize the SWEET framework to the tabular and the low-rank MDP settings, and develop algorithms coined Tabular-SWEET and Low-rank-SWEET, respectively. Both algorithms leverage the concavity and continuity of the newly introduced truncated value functions, and are guaranteed to achieve zero constraint violation during exploration with high probability. Furthermore, both algorithms can provably find a near-optimal policy subject to any constraint in the planning phase. Remarkably, the sample complexities under both algorithms match or even outperform the state of the art in their constraint-free counterparts up to some constant factors, proving that safety constraint hardly increases the sample complexity for RF-RL. | https://openreview.net/pdf/4d89566649ca26b4992a3fab78d893feb9ca8dc1.pdf |
Diffusion-based Image Translation using disentangled style and content representation | https://openreview.net/forum?id=Nayau9fwXU | https://openreview.net/forum?id=Nayau9fwXU | Gihyun Kwon,Jong Chul Ye | ICLR 2023,Poster | Diffusion-based image translation guided by semantic texts or a single target image has enabled flexible style transfer which is not limited to the specific domains.
Unfortunately, due to the stochastic nature of diffusion models, it is often difficult to maintain the original content of the image during the reverse diffusion.
To address this, here we present a novel diffusion-based unsupervised image translation method, dubbed as DiffuseIT, using disentangled style and content representation.
Specifically, inspired by the slicing Vision Transformer, we extract intermediate keys of multihead self attention layer from ViT model and used them as the content preservation loss. Then, an image guided style transfer is performed by matching the [CLS] classification token from the denoised samples and target image, whereas additional CLIP loss is used for the text-driven style transfer.
To further accelerate the semantic change during the reverse diffusion, we also propose a novel semantic divergence loss and resampling strategy.
Our experimental results show that the proposed method outperforms state-of-the-art baseline models in both text-guided and image-guided translation tasks. | https://openreview.net/pdf/b3174c74984a2e90538982e09e40225a474d34e0.pdf |
Implicit Regularization for Group Sparsity | https://openreview.net/forum?id=d7Q0vVfJ0wO | https://openreview.net/forum?id=d7Q0vVfJ0wO | Jiangyuan Li,Thanh V Nguyen,Chinmay Hegde,Raymond K. W. Wong | ICLR 2023,Poster | We study the implicit regularization of gradient descent towards structured sparsity via a novel neural reparameterization, which we call a diagonally grouped linear neural network. We show the following intriguing property of our reparameterization: gradient descent over the squared regression loss, without any explicit regularization, biases towards solutions with a group sparsity structure. In contrast to many existing works in understanding implicit regularization, we prove that our training trajectory cannot be simulated by mirror descent. We analyze the gradient dynamics of the corresponding regression problem in the general noise setting and obtain minimax-optimal error rates. Compared to existing bounds for implicit sparse regularization using diagonal linear networks, our analysis with the new reparameterization shows improved sample complexity. In the degenerate case of size-one groups, our approach gives rise to a new algorithm for sparse linear regression. Finally, we demonstrate the efficacy of our approach with several numerical experiments. | https://openreview.net/pdf/efa20f315f25148216c11b749723e1e6e8fac117.pdf |
Large Language Models are Human-Level Prompt Engineers | https://openreview.net/forum?id=92gvk82DE- | https://openreview.net/forum?id=92gvk82DE- | Yongchao Zhou,Andrei Ioan Muresanu,Ziwen Han,Keiran Paster,Silviu Pitis,Harris Chan,Jimmy Ba | ICLR 2023,Poster | By conditioning on natural language instructions, large language models (LLMs) have displayed impressive capabilities as general-purpose computers. However, task performance depends significantly on the quality of the prompt used to steer the model, and most effective prompts have been handcrafted by humans. Inspired by classical program synthesis and the human approach to prompt engineering, we propose Automatic Prompt Engineer (APE) for automatic instruction generation and selection. In our method, we treat the instruction as the "program," optimized by searching over a pool of instruction candidates proposed by an LLM in order to maximize a chosen score function. To evaluate the quality of the selected instruction, we evaluate the zero-shot performance of another LLM following the selected instruction. Experiments on 24 NLP tasks show that our automatically generated instructions outperform the prior LLM baseline by a large margin and achieve better or comparable performance to the instructions generated by human annotators on 21/24 tasks. We conduct extensive qualitative and quantitative analyses to explore the performance of APE. We show that APE-engineered prompts can be applied to steer models toward truthfulness and/or informativeness, as well as to improve few-shot learning performance by simply prepending them to standard in-context learning prompts. | https://openreview.net/pdf/e7ec7c76cacea5d0272b93de5a569d872e7344e6.pdf |
Pruning Deep Neural Networks from a Sparsity Perspective | https://openreview.net/forum?id=i-DleYh34BM | https://openreview.net/forum?id=i-DleYh34BM | Enmao Diao,Ganghua Wang,Jiawei Zhang,Yuhong Yang,Jie Ding,Vahid Tarokh | ICLR 2023,Poster | In recent years, deep network pruning has attracted significant attention in order to enable the rapid deployment of AI into small devices with computation and memory constraints. Pruning is often achieved by dropping redundant weights, neurons, or layers of a deep network while attempting to retain a comparable test performance. Many deep pruning algorithms have been proposed with impressive empirical success. However, existing approaches lack a quantifiable measure to estimate the compressibility of a sub-network during each pruning iteration and thus may under-prune or over-prune the model. In this work, we propose PQ Index (PQI) to measure the potential compressibility of deep neural networks and use this to develop a Sparsity-informed Adaptive Pruning (SAP) algorithm. Our extensive experiments corroborate the hypothesis that for a generic pruning procedure, PQI decreases first when a large model is being effectively regularized and then increases when its compressibility reaches a limit that appears to correspond to the beginning of underfitting. Subsequently, PQI decreases again when the model collapse and significant deterioration in the performance of the model start to occur. Additionally, our experiments demonstrate that the proposed adaptive pruning algorithm with proper choice of hyper-parameters is superior to the iterative pruning algorithms such as the lottery ticket-based pruning methods, in terms of both compression efficiency and robustness. | https://openreview.net/pdf/652444c01246540d7c8df32b03e3bd456fc28511.pdf |
Enhancing Meta Learning via Multi-Objective Soft Improvement Functions | https://openreview.net/forum?id=hCmjBJeGXcu | https://openreview.net/forum?id=hCmjBJeGXcu | Runsheng Yu,Weiyu Chen,Xinrun Wang,James Kwok | ICLR 2023,Poster | Meta-learning tries to leverage information from similar learning tasks. In the commonly-used bilevel optimization formulation, the shared parameter is learned in the outer loop by minimizing the average loss over all tasks. However, the converged solution may be comprised in that it only focuses on optimizing on a small subset of tasks. To alleviate this problem, we consider meta-learning as a multi-objective optimization (MOO) problem, in which each task is an objective. However, existing MOO solvers need to access all the objectives’ gradients in each iteration, and cannot scale to the huge number of tasks in typical meta-learning settings. To alleviate this problem, we propose a scalable gradient-based solver with the use of mini-batch. We provide theoretical guarantees on the Pareto optimality or Pareto stationarity of the converged solution. Empirical studies on various machine learning settings demonstrate that the proposed method is efficient, and achieves better performance than the baselines, particularly on improving the performance of the poorly-performing tasks and thus alleviating the compromising phenomenon. | https://openreview.net/pdf/f53bb21f36a339d8e616b517696e640b5fbfa3f3.pdf |
Discrete Predictor-Corrector Diffusion Models for Image Synthesis | https://openreview.net/forum?id=VM8batVBWvg | https://openreview.net/forum?id=VM8batVBWvg | Jose Lezama,Tim Salimans,Lu Jiang,Huiwen Chang,Jonathan Ho,Irfan Essa | ICLR 2023,Poster | We introduce Discrete Predictor-Corrector diffusion models (DPC), extending predictor-corrector samplers in Gaussian diffusion models to the discrete case. Predictor-corrector samplers are a class of samplers for diffusion models, which improve on ancestral samplers by correcting the sampling distribution of intermediate diffusion states using MCMC methods. In DPC, the Langevin corrector, which does not have a direct counterpart in discrete space, is replaced with a discrete MCMC transition defined by a learned corrector kernel. The corrector kernel is trained to make the correction steps achieve asymptotic convergence, in distribution, to the correct marginal of the intermediate diffusion states. Equipped with DPC, we revisit recent transformer-based non-autoregressive generative models through the lens of discrete diffusion, and find that DPC can alleviate the compounding decoding error due to the parallel sampling of visual tokens. Our experiments show that DPC improves upon existing discrete latent space models for class-conditional image generation on ImageNet, and outperforms continuous diffusion models and GANs, according to standard metrics and user preference studies. | https://openreview.net/pdf/11def978889827cebd99ce8e154f1d7a8ba62dde.pdf |
OPTQ: Accurate Quantization for Generative Pre-trained Transformers | https://openreview.net/forum?id=tcbBPnfwxS | https://openreview.net/forum?id=tcbBPnfwxS | Elias Frantar,Saleh Ashkboos,Torsten Hoefler,Dan Alistarh | ICLR 2023,Poster | Generative Pre-trained Transformer models, known as GPT or OPT, set themselves apart through breakthrough performance across complex language modelling tasks, but also by their extremely high computational and storage costs. Specifically, due to their massive size, even inference for large, highly-accurate GPT models may require multiple performant GPUs, which limits the usability of such models. While there is emerging work on relieving this pressure via model compression, the applicability and performance of existing compression techniques is limited by the scale and complexity of GPT models. In this paper, we address this challenge, and propose OPTQ, a new one-shot weight quantization method based on approximate second-order information, that is both highly-accurate and highly-efficient. Specifically, OPTQ can quantize GPT models with 175 billion parameters in approximately four GPU hours, reducing the bitwidth down to 3 or 4 bits per weight, with negligible accuracy degradation relative to the uncompressed baseline. Our method more than doubles the compression gains relative to previously-proposed one-shot quantization methods, preserving accuracy, allowing us for the first time to execute an 175 billion-parameter model inside a single GPU for generative inference. Moreover, we also show that our method can still provide reasonable accuracy in the extreme quantization regime, in which weights are quantized to 2-bit or even ternary quantization levels.
We show experimentally that these improvements can be leveraged for end-to-end inference speedups over FP16, of around 3.25x when using high-end GPUs (NVIDIA A100) and 4.5x when using more cost-effective ones (NVIDIA A6000). The implementation is available at https://github.com/IST-DASLab/gptq. | https://openreview.net/pdf/f99f2d5ea7fda817912034e810f9e385d2add0e1.pdf |
A new characterization of the edge of stability based on a sharpness measure aware of batch gradient distribution | https://openreview.net/forum?id=bH-kCY6LdKg | https://openreview.net/forum?id=bH-kCY6LdKg | Sungyoon Lee,Cheongjae Jang | ICLR 2023,Poster | For full-batch gradient descent (GD), it has been empirically shown that the sharpness, the top eigenvalue of the Hessian, increases and then hovers above $2/\text{(learning rate)}$, and this is called ``the edge of stability'' phenomenon. However, it is unclear why the sharpness is somewhat larger than $2/\text{(learning rate)}$ and how this can be extended to general mini-batch stochastic gradient descent (SGD). We propose a new sharpness measure (interaction-aware-sharpness) aware of the \emph{interaction} between the batch gradient distribution and the loss landscape geometry. This leads to a more refined and general characterization of the edge of stability for SGD. Moreover, based on the analysis of a concentration measure of the batch gradient, we propose a more accurate scaling rule, Linear and Saturation Scaling Rule (LSSR), between batch size and learning rate. | https://openreview.net/pdf/aff9dfed9a6951af8497a002a9fbee60300ff25b.pdf |
$\mathrm{SE}(3)$-Equivariant Attention Networks for Shape Reconstruction in Function Space | https://openreview.net/forum?id=RDy3IbvjMqT | https://openreview.net/forum?id=RDy3IbvjMqT | Evangelos Chatzipantazis,Stefanos Pertigkiozoglou,Edgar Dobriban,Kostas Daniilidis | ICLR 2023,Poster | We propose a method for 3D shape reconstruction from unoriented point clouds. Our method consists of a novel SE(3)-equivariant coordinate-based network (TF-ONet), that parametrizes the occupancy field of the shape and respects the inherent symmetries of the problem. In contrast to previous shape reconstruction methods that align the input to a regular grid, we operate directly on the irregular point cloud. Our architecture leverages equivariant attention layers that operate on local tokens. This mechanism enables local shape modelling, a crucial property for scalability to large scenes. Given an unoriented, sparse, noisy point cloud as input, we produce equivariant features for each point. These serve as keys and values for the subsequent equivariant cross-attention blocks that parametrize the occupancy field. By querying an arbitrary point in space, we predict its occupancy score. We show that our method outperforms previous SO(3)-equivariant methods, as well as non-equivariant methods trained on SO(3)-augmented datasets. More importantly, local modelling together with SE(3)-equivariance create an ideal setting for SE(3) scene reconstruction. We show that by training only on single, aligned objects and without any pre-segmentation, we can reconstruct novel scenes containing arbitrarily many objects in random poses without any performance loss.
| https://openreview.net/pdf/9411d2b932a2b7a46202e67d58a5f8067fd33c77.pdf |
Continual Pre-training of Language Models | https://openreview.net/forum?id=m_GDIItaI3o | https://openreview.net/forum?id=m_GDIItaI3o | Zixuan Ke,Yijia Shao,Haowei Lin,Tatsuya Konishi,Gyuhak Kim,Bing Liu | ICLR 2023,Poster | Language models (LMs) have been instrumental for the rapid advance of natural language processing. This paper studies continual pre-training of LMs, in particular, continual domain-adaptive pre-training (or continual DAP-training). Existing research has shown that further pre-training an LM using a domain corpus to adapt the LM to the domain can improve the end-task performance in the domain. This paper proposes a novel method to continually DAP-train an LM with a sequence of unlabeled domain corpora to adapt the LM to these domains to improve their end-task performances. The key novelty of our method is a soft-masking mechanism that directly controls the update to the LM. A novel proxy is also proposed to preserve the general knowledge in the original LM. Additionally, it contrasts the representations of the previously learned domain knowledge (including the general knowledge in the pre-trained LM) and the knowledge from the current full network to achieve knowledge integration. The method not only overcomes catastrophic forgetting, but also achieves knowledge transfer to improve end-task performances. Empirical evaluation demonstrates the effectiveness of the proposed method. | https://openreview.net/pdf/9ac4b567f0aac42656ddf1612df4382c308719f1.pdf |
Min-Max Multi-objective Bilevel Optimization with Applications in Robust Machine Learning | https://openreview.net/forum?id=PvDY71zKsvP | https://openreview.net/forum?id=PvDY71zKsvP | Alex Gu,Songtao Lu,Parikshit Ram,Tsui-Wei Weng | ICLR 2023,Poster | We consider a generic min-max multi-objective bilevel optimization problem with applications in robust machine learning such as representation learning and hyperparameter optimization. We design MORBiT, a novel single-loop gradient descent-ascent bilevel optimization algorithm, to solve the generic problem and present a novel analysis showing that MORBiT converges to the first-order stationary point at a rate of $\widetilde{\mathcal{O}}(n^{1/2} K^{-2/5})$ for a class of weakly convex problems with $n$ objectives upon $K$ iterations of the algorithm. Our analysis utilizes novel results to handle the non-smooth min-max multi-objective setup and to obtain a sublinear dependence in the number of objectives $n$. Experimental results on robust representation learning and robust hyperparameter optimization showcase (i) the advantages of considering the min-max multi-objective setup, and (ii) convergence properties of the proposed \morbit. | https://openreview.net/pdf/1c2ae9cddfcd50d649774b6e7926db6adae07eaf.pdf |
Forward Super-Resolution: How Can GANs Learn Hierarchical Generative Models for Real-World Distributions | https://openreview.net/forum?id=7h5KSs2PCRi | https://openreview.net/forum?id=7h5KSs2PCRi | Zeyuan Allen-Zhu,Yuanzhi Li | ICLR 2023,Poster | Generative adversarial networks (GANs) are among the most successful models for learning high-complexity, real-world distributions. However, in theory, due to the highly non-convex, non-concave landscape of the minmax training objective, GAN remains one of the least understood deep learning models. In this work, we formally study how GANs can efficiently learn certain hierarchically generated distributions that are close to the distribution of real-life images. We prove that when a distribution has a structure that we refer to as \emph{forward super-resolution}, then simply training generative adversarial networks using stochastic gradient descent ascent (SGDA) can learn this distribution efficiently, both in sample and time complexities.
We also provide empirical evidence that our assumption ``forward super-resolution'' is very natural in practice, and the underlying learning mechanisms that we study in this paper (to allow us efficiently train GAN via GDA in theory) simulates the actual learning process of GANs on real-world problems.
| https://openreview.net/pdf/4dd55b5026c20f0d5eeeae3b1bc2b2decd36b47b.pdf |
Spotlight: Mobile UI Understanding using Vision-Language Models with a Focus | https://openreview.net/forum?id=9yE2xEj0BH7 | https://openreview.net/forum?id=9yE2xEj0BH7 | Gang Li,Yang Li | ICLR 2023,Poster | Mobile UI understanding is important for enabling various interaction tasks such as UI automation and accessibility. Previous mobile UI modeling often depends on the view hierarchy information of a screen, which directly provides the structural data of the UI, with the hope to bypass challenging tasks of visual modeling from screen pixels. However, view hierarchies are not always available, and are often corrupted with missing object descriptions or misaligned structure information. As a result, despite the use of view hierarchies could offer short-term gains, it may ultimately hinder the applicability and performance of the model. In this paper, we propose Spotlight, a vision-only approach for mobile UI understanding. Specifically, we enhance a vision-language model that only takes the screenshot of the UI and a region of interest on the screen---the focus---as the input. This general architecture of Spotlight is easily scalable and capable of performing a range of UI modeling tasks. Our experiments show that our model establishes SoTA results on several representative UI tasks and outperforms previous methods that use both screenshots and view hierarchies as inputs. Furthermore, we explore multi-task learning and few-shot prompting capacities of the proposed models, demonstrating promising results in the multi-task learning direction. | https://openreview.net/pdf/ad305a1a4c4b0a2571863546dc680f91c8b5b9f1.pdf |
A Control-Centric Benchmark for Video Prediction | https://openreview.net/forum?id=rimcq1oIFeR | https://openreview.net/forum?id=rimcq1oIFeR | Stephen Tian,Chelsea Finn,Jiajun Wu | ICLR 2023,Poster | Video is a promising source of knowledge for embodied agents to learn models of the world's dynamics. Large deep networks have become increasingly effective at modeling complex video data in a self-supervised manner, as evaluated by metrics based on human perceptual similarity or pixel-wise comparison. However, it remains unclear whether current metrics are accurate indicators of performance on downstream tasks. We find empirically that for planning robotic manipulation, existing metrics can be unreliable at predicting execution success. To address this, we propose a benchmark for action-conditioned video prediction in the form of a control benchmark that evaluates a given model for simulated robotic manipulation through sampling-based planning. Our benchmark, Video Prediction for Visual Planning ($\text{VP}^2$), includes simulated environments with $11$ task categories and $310$ task instance definitions, a full planning implementation, and training datasets containing scripted interaction trajectories for each task category. A central design goal of our benchmark is to expose a simple interface -- a single forward prediction call -- so it is straightforward to evaluate almost any action-conditioned video prediction model. We then leverage our benchmark to study the effects of scaling model size, quantity of training data, and model ensembling by analyzing five highly-performant video prediction models, finding that while scale can improve perceptual quality when modelling visually diverse settings, other attributes such as uncertainty awareness can also aid planning performance. | https://openreview.net/pdf/8c161f82c74baef9a6fabc2af0cc7eb10f308add.pdf |
A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks | https://openreview.net/forum?id=vsMyHUq_C1c | https://openreview.net/forum?id=vsMyHUq_C1c | Marc Anton Finzi,Andres Potapczynski,Matthew Choptuik,Andrew Gordon Wilson | ICLR 2023,Poster | Unlike conventional grid and mesh based methods for solving partial differential equations (PDEs), neural networks have the potential to break the curse of dimensionality, providing approximate solutions to problems where using classical solvers is difficult or impossible. While global minimization of the PDE residual over the network parameters works well for boundary value problems, catastrophic forgetting impairs applicability to initial value problems (IVPs). In an alternative local-in-time approach, the optimization problem can be converted into an ordinary differential equation (ODE) on the network parameters and the solution propagated forward in time; however, we demonstrate that current methods based on this approach suffer from two key issues. First, following the ODE produces an uncontrolled growth in the conditioning of the problem, ultimately leading to unacceptably large numerical errors. Second, as the ODE methods scale cubically with the number of model parameters, they are restricted to small neural networks, significantly limiting their ability to represent intricate PDE initial conditions and solutions. Building on these insights, we develop Neural-IVP, an ODE based IVP solver which prevents the network from getting ill-conditioned and runs in time linear in the number of parameters, enabling us to evolve the dynamics of challenging PDEs with neural networks. | https://openreview.net/pdf/29b7a937e4233d944f06cc530414bef7be4d941b.pdf |
Noise Is Not the Main Factor Behind the Gap Between Sgd and Adam on Transformers, But Sign Descent Might Be | https://openreview.net/forum?id=a65YK0cqH8g | https://openreview.net/forum?id=a65YK0cqH8g | Frederik Kunstner,Jacques Chen,Jonathan Wilder Lavington,Mark Schmidt | ICLR 2023,Poster | The success of the Adam optimizer on a wide array of architectures has made it the default in settings where stochastic gradient descent (SGD) performs poorly. However, our theoretical understanding of this discrepancy is lagging, preventing the development of significant improvements on either algorithm. Recent work advances the hypothesis that Adam and other heuristics like gradient clipping outperform SGD on language tasks because the distribution of the error induced by sampling has heavy tails. This suggests that Adam outperform SGD because it uses a more robust gradient estimate. We evaluate this hypothesis by varying the batch size, up to the entire dataset, to control for stochasticity. We present evidence that stochasticity and heavy-tailed noise are not major factors in the performance gap between SGD and Adam. Rather, Adam performs better as the batch size increases, while SGD is less effective at taking advantage of the reduction in noise. This raises the question as to why Adam outperforms SGD in the full-batch setting. Through further investigation of simpler variants of SGD, we find that the behavior of Adam with large batches is similar to sign descent with momentum. | https://openreview.net/pdf/a297584062046048f65e016f4627d021b50ef011.pdf |
Building Normalizing Flows with Stochastic Interpolants | https://openreview.net/forum?id=li7qeBbCR1t | https://openreview.net/forum?id=li7qeBbCR1t | Michael Samuel Albergo,Eric Vanden-Eijnden | ICLR 2023,Poster | A generative model based on a continuous-time normalizing flow between any pair of base and target probability densities is proposed. The velocity field of this flow is inferred from the probability current of a time-dependent density that interpolates between the base and the target in finite time. Unlike conventional normalizing flow inference methods based the maximum likelihood principle, which require costly backpropagation through ODE solvers, our interpolant approach leads to a simple quadratic loss for the velocity itself which is expressed in terms of expectations that are readily amenable to empirical estimation. The flow can be used to generate samples from either the base or target, and to estimate the likelihood at any time along the interpolant. In addition, the flow can be optimized to minimize the path length of the interpolant density, thereby paving the way for building optimal transport maps. In situations where the base is a Gaussian density, we also show that the velocity of our normalizing flow can also be used to construct a diffusion model to sample the target as well as estimate its score. However, our approach shows that we can bypass this diffusion completely and work at the level of the probability flow with greater simplicity, opening an avenue for methods based solely on ordinary differential equations as an alternative to those based on stochastic differential equations. Benchmarking on density estimation tasks illustrates that the learned flow can match and surpass conventional continuous flows at a fraction of the cost, and compares well with diffusions on image generation on CIFAR-10 and ImageNet $32 \times 32$. The method scales ab-initio ODE flows to previously unreachable image resolutions, demonstrated up to $128\times128$. | https://openreview.net/pdf/45d09331dca3b57160a64f0922ee4841585e15b5.pdf |
Dual Student Networks for Data-Free Model Stealing | https://openreview.net/forum?id=VE1s3e5xriA | https://openreview.net/forum?id=VE1s3e5xriA | James Beetham,Navid Kardan,Ajmal Saeed Mian,Mubarak Shah | ICLR 2023,Poster | Data-free model stealing aims to replicate a target model without direct access to either the training data or the target model. To accomplish this, existing methods use a generator to produce samples in order to train a student model to match the target model outputs. To this end, the two main challenges are estimating gradients of the target model without access to its parameters, and generating a diverse set of training samples that thoroughly explores the input space. We propose a Dual Student method where two students are symmetrically trained in order to provide the generator a criterion to generate samples that the two students disagree on. On one hand, disagreement on a sample implies at least one student has classified the sample incorrectly when compared to the target model. This incentive towards disagreement implicitly encourages the generator to explore more diverse regions of the input space. On the other hand, our method utilizes gradients of student models to indirectly estimate gradients of the target model. We show that this novel training objective for the generator network is equivalent to optimizing a lower bound on the generator's loss if we had access to the target model gradients. In other words, our method alters the standard data-free model stealing paradigm by substituting the target model with a separate student model, thereby creating a lower bound which can be directly optimized without additional target model queries or separate synthetic datasets. We show that our new optimization framework provides more accurate gradient estimation of the target model and better accuracies on benchmark classification datasets. Additionally, our approach balances improved query efficiency with training computation cost. Finally, we demonstrate that our method serves as a better proxy model for transfer-based adversarial attacks than existing data-free model stealing methods. | https://openreview.net/pdf/41d38335bded64c06dfd2672ead107cdf3b3e2d9.pdf |
Composite Slice Transformer: An Efficient Transformer with Composition of Multi-Scale Multi-Range Attentions | https://openreview.net/forum?id=nWTzIsgrYNN | https://openreview.net/forum?id=nWTzIsgrYNN | Mingu Lee,Saurabh Pitre,Tianyu Jiang,Pierre-David Letourneau,Matthew J Morse,Kanghwan Jang,Joseph Soriaga,Parham Noorzad,Hsin-Pai Cheng,Christopher Lott | ICLR 2023,Poster | Since the introduction of Transformers, researchers have tackled the notoriously expensive quadratic complexity problem. While significant computational efficiency improvements have been achieved, they come at the cost of reduced accuracy trade-offs. In this paper, we propose Composite Slice Transformer (CST), a Transformer-based network equipped with a composition of multi-scale multi-range attentions, boosting both efficiency and modeling capability.
After stacking fixed-length slices of the input sequence, each layer in CST performs a pair of fine-and-coarse-grained attentions with short-long ranges in a sequential manner, coupled with volatile instant positional embedding, enabling efficient token interactions {\em and} improving expressiveness of the model.
In addition to significantly reduced $O(NL+N^2/L^2)$ complexity for sequence length $N$ and slice length $L$, CST achieves superior performance on a variety of tasks. We show that CST surpasses recently published efficient Transformers on the Long Range Arena benchmark, demonstrating the bidirectional long-range dependency modeling capability of our model. It outperforms the standard Transformer by a margin of $6.9$\% in average accuracy across the five classification tasks of the benchmark, while being of complexity comparable to other efficient transformers. Furthermore, on the word-level autoregressive language modeling task with the WikiText-103 dataset, CST performs competitively against the Transformer model with only $2$\% gap in the test perplexity while outperforming other efficient Transformers. | https://openreview.net/pdf/156f56ba50a9889bf9fae0ce2e66f3c3e345c7b8.pdf |
Equal Improvability: A New Fairness Notion Considering the Long-term Impact | https://openreview.net/forum?id=dhYUMMy0_Eg | https://openreview.net/forum?id=dhYUMMy0_Eg | Ozgur Guldogan,Yuchen Zeng,Jy-yong Sohn,Ramtin Pedarsani,Kangwook Lee | ICLR 2023,Poster | Devising a fair classifier that does not discriminate against different groups is an important problem in machine learning. Although researchers have proposed various ways of defining group fairness, most of them only focused on the immediate fairness, ignoring the long-term impact of a fair classifier under the dynamic scenario where each individual can improve its feature over time. Such dynamic scenarios happen in real world, e.g., college admission and credit loaning, where each rejected sample makes effort to change its features to get accepted afterwards. In this dynamic setting, the long-term fairness should equalize the samples’ feature distribution across different groups after the rejected samples make some effort to improve. In order to promote long-term fairness, we propose a new fairness notion called Equal Improvability (EI), which equalizes the potential acceptance rate of the rejected samples across different groups assuming a bounded level of effort will be spent by each rejected sample. We analyze the properties of EI and its connections with existing fairness notions. To find a classifier that satisfies the EI requirement, we propose and study three different approaches that solve EI regularized optimization problems. Through experiments on both synthetic and real datasets, we demonstrate that the proposed EI-regularized algorithms encourage us to find a fair classifier in terms of EI. Finally, we provide experimental results on dynamic scenarios which highlight the advantages of our EI metric in achieving the long-term fairness. Codes are available in anonymous GitHub repository. | https://openreview.net/pdf/97ea07ad031609a62cd7a8f682fec684434f45cf.pdf |
Competitive Physics Informed Networks | https://openreview.net/forum?id=z9SIj-IM7tn | https://openreview.net/forum?id=z9SIj-IM7tn | Qi Zeng,Yash Kothari,Spencer H Bryngelson,Florian Tobias Schaefer | ICLR 2023,Poster | Neural networks can be trained to solve partial differential equations (PDEs) by using the PDE residual as the loss function. This strategy is called "physics-informed neural networks" (PINNs), but it currently cannot produce high-accuracy solutions, typically attaining about $0.1\%$ relative error. We present an adversarial approach that overcomes this limitation, which we call competitive PINNs (CPINNs). CPINNs train a discriminator that is rewarded for predicting mistakes the PINN makes. The discriminator and PINN participate in a zero-sum game with the exact PDE solution as an optimal strategy. This approach avoids squaring the large condition numbers of PDE discretizations, which is the likely reason for failures of previous attempts to decrease PINN errors even on benign problems. Numerical experiments on a Poisson problem show that CPINNs achieve errors four orders of magnitude smaller than the best-performing PINN. We observe relative errors on the order of single-precision accuracy, consistently decreasing with each epoch. To the authors' knowledge, this is the first time this level of accuracy and convergence behavior has been achieved. Additional experiments on the nonlinear Schr{\"o}dinger, Burgers', and Allen--Cahn equation show that the benefits of CPINNs are not limited to linear problems. | https://openreview.net/pdf/feb4b9be1c1d0679cf4c1f97725f3ae7e2e8d153.pdf |
Decomposed Prompting: A Modular Approach for Solving Complex Tasks | https://openreview.net/forum?id=_nGgzQjzaRy | https://openreview.net/forum?id=_nGgzQjzaRy | Tushar Khot,Harsh Trivedi,Matthew Finlayson,Yao Fu,Kyle Richardson,Peter Clark,Ashish Sabharwal | ICLR 2023,Poster | Few-shot prompting is a surprisingly powerful way to use Large Language Models (LLMs) to solve various tasks. However, this approach struggles as the task complexity increases or when the individual reasoning steps of the task themselves are hard to learn, especially when embedded in more complex tasks. To address this, we propose Decomposed Prompting, a new approach to solve complex tasks by decomposing them (via prompting) into simpler sub-tasks that can be delegated to a library of prompting-based LLMs dedicated to these sub-tasks. This modular structure allows each prompt to be optimized for its specific sub-task, further decomposed if necessary, and even easily replaced with more effective prompts, trained models, or symbolic functions if desired.
We show that the flexibility and modularity of Decomposed Prompting allows it to outperform prior work on few-shot prompting using GPT3. On symbolic reasoning tasks, we can further decompose sub-tasks that are hard for LLMs into even simpler solvable sub-tasks. When the complexity comes from the input length, we can recursively decompose the task into the same task but with smaller inputs. We also evaluate our approach on textual multi-step reasoning tasks: on long-context multi-hop QA task, we can more effectively teach the sub-tasks via our separate sub-tasks prompts; and on open-domain multi-hop QA, we can incorporate a symbolic information retrieval within our decomposition framework, leading to improved performance on both tasks. Datasets, Code and Prompts available at https://github.com/allenai/DecomP. | https://openreview.net/pdf/c0a9bac140b181384176f474f3533e053b8d663d.pdf |
Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors | https://openreview.net/forum?id=9MO7bjoAfIA | https://openreview.net/forum?id=9MO7bjoAfIA | Sizhe Chen,Geng Yuan,Xinwen Cheng,Yifan Gong,Minghai Qin,Yanzhi Wang,Xiaolin Huang | ICLR 2023,Poster | As data becomes increasingly vital, a company would be very cautious about releasing data, because the competitors could use it to train high-performance models, thereby posing a tremendous threat to the company's commercial competence. To prevent training good models on the data, we could add imperceptible perturbations to it. Since such perturbations aim at hurting the entire training process, they should reflect the vulnerability of DNN training, rather than that of a single model. Based on this new idea, we seek perturbed examples that are always unrecognized (never correctly classified) in training. In this paper, we uncover them by model checkpoints' gradients, forming the proposed self-ensemble protection (SEP), which is very effective because (1) learning on examples ignored during normal training tends to yield DNNs ignoring normal examples; (2) checkpoints' cross-model gradients are close to orthogonal, meaning that they are as diverse as DNNs with different architectures. That is, our amazing performance of ensemble only requires the computation of training one model. By extensive experiments with 9 baselines on 3 datasets and 5 architectures, SEP is verified to be a new state-of-the-art, e.g., our small $\ell_\infty=2/255$ perturbations reduce the accuracy of a CIFAR-10 ResNet18 from 94.56% to 14.68%, compared to 41.35% by the best-known method. Code is available at https://github.com/Sizhe-Chen/SEP. | https://openreview.net/pdf/03e69010cd3d91e35491934b95bbd8839f6416bc.pdf |
Effectively Modeling Time Series with Simple Discrete State Spaces | https://openreview.net/forum?id=2EpjkjzdCAa | https://openreview.net/forum?id=2EpjkjzdCAa | Michael Zhang,Khaled Kamal Saab,Michael Poli,Tri Dao,Karan Goel,Christopher Re | ICLR 2023,Poster | Time series modeling is a well-established problem, which often requires that methods (1) expressively represent complicated dependencies, (2) forecast long horizons, and (3) efficiently train over long sequences. State-space models (SSMs) are classical models for time series, and prior works combine SSMs with deep learning layers for efficient sequence modeling. However, we find fundamental limitations with these prior approaches, proving their SSM representations cannot express autoregressive time series processes. We thus introduce SpaceTime, a new state-space time series architecture that improves all three criteria. For expressivity, we propose a new SSM parameterization based on the companion matrix---a canonical representation for discrete-time processes---which enables SpaceTime's SSM layers to learn desirable autoregressive processes. For long horizon forecasting, we introduce a "closed-loop" variation of the companion SSM, which enables SpaceTime to predict many future time-steps by generating its own layer-wise inputs. For efficient training and inference, we introduce an algorithm that reduces the memory and compute of a forward pass with the companion matrix. With sequence length $\ell$ and state-space size $d$, we go from $\tilde{O}(d \ell)$ naïvely to $\tilde{O}(d + \ell)$. In experiments, our contributions lead to state-of-the-art results on extensive and diverse benchmarks, with best or second-best AUROC on 6 / 7 ECG and speech time series classification, and best MSE on 14 / 16 Informer forecasting tasks. Furthermore, we find SpaceTime (1) fits AR($p$) processes that prior deep SSMs fail on, (2) forecasts notably more accurately on longer horizons than prior state-of-the-art, and (3) speeds up training on real-world ETTh1 data by 73% and 80% relative wall-clock time over Transformers and LSTMs. | https://openreview.net/pdf/ab3d042895227ba8357ce14f5695c199eaf81a23.pdf |
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers | https://openreview.net/forum?id=Jbdc0vTOcol | https://openreview.net/forum?id=Jbdc0vTOcol | Yuqi Nie,Nam H Nguyen,Phanwadee Sinthong,Jayant Kalagnanam | ICLR 2023,Poster | We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning. It is based on two key components: (i) segmentation of time series into subseries-level patches which are served as input tokens to Transformer; (ii) channel-independence where each channel contains a single univariate time series that shares the same embedding and Transformer weights across all the series. Patching design naturally has three-fold benefit: local semantic information is retained in the embedding; computation and memory usage of the attention maps are quadratically reduced given the same look-back window; and the model can attend longer history. Our channel-independent patch time series Transformer (PatchTST) can improve the long-term forecasting accuracy significantly when compared with that of SOTA Transformer-based models. We also apply our model to self-supervised pre-training tasks and attain excellent fine-tuning performance, which outperforms supervised training on large datasets. Transferring of masked pre-training performed on one dataset to other datasets also produces SOTA forecasting accuracy. | https://openreview.net/pdf/2e4e6db8733d24f382a7e57c9b3d53d7e0061ade.pdf |
Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue Systems | https://openreview.net/forum?id=086pmarAris | https://openreview.net/forum?id=086pmarAris | Yihao Feng,Shentao Yang,Shujian Zhang,Jianguo Zhang,Caiming Xiong,Mingyuan Zhou,Huan Wang | ICLR 2023,Poster | When learning task-oriented dialogue (ToD) agents, reinforcement learning (RL) techniques can naturally be utilized to train dialogue strategies to achieve user-specific goals. Prior works mainly focus on adopting advanced RL techniques to train the ToD agents, while the design of the reward function is not well studied. This paper aims at answering the question of how to efficiently learn and leverage a reward function for training end-to-end (E2E) ToD agents. Specifically, we introduce two generalized objectives for reward-function learning, inspired by the classical learning-to-rank literature. Further, we utilize the learned reward function to guide the training of the E2E ToD agent. With the proposed techniques, we achieve competitive results on the E2E response-generation task on the Multiwoz 2.0 dataset. Source code and checkpoints are publicly released at https://github.com/Shentao-YANG/Fantastic_Reward_ICLR2023. | https://openreview.net/pdf/a1290714106d4bdf27febffa982192b6b9d5819f.pdf |
Supervision Complexity and its Role in Knowledge Distillation | https://openreview.net/forum?id=8jU7wy7N7mA | https://openreview.net/forum?id=8jU7wy7N7mA | Hrayr Harutyunyan,Ankit Singh Rawat,Aditya Krishna Menon,Seungyeon Kim,Sanjiv Kumar | ICLR 2023,Poster | Despite the popularity and efficacy of knowledge distillation, there is limited understanding of why it helps. In order to study the generalization behavior of a distilled student, we propose a new theoretical framework that leverages supervision complexity: a measure of alignment between teacher-provided supervision and the student's neural tangent kernel. The framework highlights a delicate interplay among the teacher's accuracy, the student's margin with respect to the teacher predictions, and the complexity of the teacher predictions. Specifically, it provides a rigorous justification for the utility of various techniques that are prevalent in the context of distillation, such as early stopping and temperature scaling. Our analysis further suggests the use of online distillation, where a student receives increasingly more complex supervision from teachers in different stages of their training. We demonstrate efficacy of online distillation and validate the theoretical findings on a range of image classification benchmarks and model architectures. | https://openreview.net/pdf/e129ac7ac66f9fbb8997557423dfd3fe9e4ca72c.pdf |
Transferable Unlearnable Examples | https://openreview.net/forum?id=-htnolWDLvP | https://openreview.net/forum?id=-htnolWDLvP | Jie Ren,Han Xu,Yuxuan Wan,Xingjun Ma,Lichao Sun,Jiliang Tang | ICLR 2023,Poster | With more people publishing their personal data online, unauthorized data usage has become a serious concern. The unlearnable examples strategies have been introduced to prevent third parties from training on the data without permission. They add perturbations to the users’ data before publishing, so as to make the models trained on the perturbed published dataset invalidated. These perturbations have been generated for a specific training setting and a target dataset. However, their unlearnable effects significantly decrease when used in other training settings or datasets. To tackle this issue, we propose a novel unlearnable strategy based on Class-wise Separability Discriminant (CSD), which boosts the transferability of the unlearnable perturbations by enhancing the linear separability. Extensive experiments demonstrate the transferability of the unlearnable examples crafted by our proposed method across training settings and datasets. | https://openreview.net/pdf/f1560bffa01d96ba8fa3a391e8b64971dad2d5e6.pdf |
Random Laplacian Features for Learning with Hyperbolic Space | https://openreview.net/forum?id=3pfNb4pZBNp | https://openreview.net/forum?id=3pfNb4pZBNp | Tao Yu,Christopher De Sa | ICLR 2023,Poster | Due to its geometric properties, hyperbolic space can support high-fidelity embeddings of tree- and graph-structured data, upon which various hyperbolic networks have been developed. Existing hyperbolic networks encode geometric priors not only for the input, but also at every layer of the network. This approach involves repeatedly mapping to and from hyperbolic space, which makes these networks complicated to implement, computationally expensive to scale, and numerically unstable to train. In this paper, we propose a simpler approach: learn a hyperbolic embedding of the input, then map once from it to Euclidean space using a mapping that encodes geometric priors by respecting the isometries of hyperbolic space, and finish with a standard Euclidean network. The key insight is to use a random feature mapping via the eigenfunctions of the Laplace operator, which we show can approximate any isometry-invariant kernel on hyperbolic space. Our method can be used together with any graph neural networks: using even a linear graph model yields significant improvements in both efficiency and performance over other hyperbolic baselines in both transductive and inductive tasks. | https://openreview.net/pdf/8608f257c0eb6508e6fc46b9182bebfb04876d22.pdf |
Replay Memory as An Empirical MDP: Combining Conservative Estimation with Experience Replay | https://openreview.net/forum?id=SjzFVSJUt8S | https://openreview.net/forum?id=SjzFVSJUt8S | Hongming Zhang,Chenjun Xiao,Han Wang,Jun Jin,bo xu,Martin Müller | ICLR 2023,Poster | Experience replay, which stores transitions in a replay memory for repeated use, plays an important role of improving sample efficiency in reinforcement learning. Existing techniques such as reweighted sampling, episodic learning and reverse sweep update further process the information in the replay memory to make experience replay more efficient. In this work, we further exploit the information in the replay memory by treating it as an empirical \emph{Replay Memory MDP (RM-MDP)}. By solving it with dynamic programming, we learn a conservative value estimate that \emph{only} considers transitions observed in the replay memory. Both value and policy regularizers based on this conservative estimate are developed and integrated with model-free learning algorithms. We design the metric \textit{memory density} to measure the quality of RM-MDP. Our empirical studies quantitatively find a strong correlation between performance improvement and memory density. Our method combines \emph{Conservative Estimation with Experience Replay (CEER)}, improving sample efficiency by a large margin, especially when the memory density is high. Even when the memory density is low, such a conservative estimate can still help to avoid suicidal actions and thereby improve performance. | https://openreview.net/pdf/6565f5cf8822651bf590d8be99db1fe12f5c1da5.pdf |
Neural Causal Models for Counterfactual Identification and Estimation | https://openreview.net/forum?id=vouQcZS8KfW | https://openreview.net/forum?id=vouQcZS8KfW | Kevin Muyuan Xia,Yushu Pan,Elias Bareinboim | ICLR 2023,Poster | Evaluating hypothetical statements about how the world would be had a different course of action been taken is arguably one key capability expected from modern AI systems. Counterfactual reasoning underpins discussions in fairness, the determination of blame and responsibility, credit assignment, and regret. In this paper, we study the evaluation of counterfactual statements through neural models. Specifically, we tackle two causal problems required to make such evaluations, i.e., counterfactual identification and estimation from an arbitrary combination of observational and experimental data. First, we show that neural causal models (NCMs) are expressive enough and encode the structural constraints necessary for performing counterfactual reasoning. Second, we develop an algorithm for simultaneously identifying and estimating counterfactual distributions. We show that this algorithm is sound and complete for deciding counterfactual identification in general settings. Third, considering the practical implications of these results, we introduce a new strategy for modeling NCMs using generative adversarial networks. Simulations corroborate with the proposed methodology. | https://openreview.net/pdf/6a0e21a7680d3623cb6e5a6b4eed2167928b096f.pdf |
Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport | https://openreview.net/forum?id=vCJ9-Ri-6xU | https://openreview.net/forum?id=vCJ9-Ri-6xU | Lingkai Kong,Yuqing Wang,Molei Tao | ICLR 2023,Poster | The problem of optimization on Stiefel manifold, i.e., minimizing functions of (not necessarily square) matrices that satisfy orthogonality constraints, has been extensively studied. Yet, a new approach is proposed based on, for the first time, an interplay between thoughtfully designed continuous and discrete dynamics. It leads to a gradient-based optimizer with intrinsically added momentum. This method exactly preserves the manifold structure but does not require additional operation to keep momentum in the changing (co)tangent space, and thus has low computational cost and pleasant accuracy. Its generalization to adaptive learning rates is also demonstrated. Notable performances are observed in practical tasks. For instance, we found that placing orthogonal constraints on attention heads of trained-from-scratch Vision Transformer (Dosovitskiy et al., 2020) could markedly improve its performance, when our optimizer is used, and it is better that each head is made orthogonal within itself but not necessarily to other heads. This optimizer also makes the useful notion of Projection Robust Wasserstein Distance (Paty and Cuturi, 2019; Lin et al., 2020) for high-dim. optimal transport even more effective. | https://openreview.net/pdf/efc53aa7247c696d0a77dc0fd44bcd24095d2fee.pdf |
Information-Theoretic Diffusion | https://openreview.net/forum?id=UvmDCdSPDOW | https://openreview.net/forum?id=UvmDCdSPDOW | Xianghao Kong,Rob Brekelmans,Greg Ver Steeg | ICLR 2023,Poster | Denoising diffusion models have spurred significant gains in density modeling and image generation, precipitating an industrial revolution in text-guided AI art generation. We introduce a new mathematical foundation for diffusion models inspired by classic results in information theory that connect Information with Minimum Mean Square Error regression, the so-called I-MMSE relations. We generalize the I-MMSE relations to \emph{exactly} relate the data distribution to an optimal denoising regression problem, leading to an elegant refinement of existing diffusion bounds. This new insight leads to several improvements for probability distribution estimation, including a theoretical justification for diffusion model ensembling. Remarkably, our framework shows how continuous and discrete probabilities can be learned with the same regression objective, avoiding domain-specific generative models used in variational methods. | https://openreview.net/pdf/b48d4f49696c822ea5644e2ba131c474e0f8c770.pdf |
SIMPLE: A Gradient Estimator for k-Subset Sampling | https://openreview.net/forum?id=GPJVuyX4p_h | https://openreview.net/forum?id=GPJVuyX4p_h | Kareem Ahmed,Zhe Zeng,Mathias Niepert,Guy Van den Broeck | ICLR 2023,Poster | $k$-subset sampling is ubiquitous in machine learning, enabling regularization and interpretability through sparsity. The challenge lies in rendering $k$-subset sampling amenable to end-to-end learning. This has typically involved relaxing the reparameterized samples to allow for backpropagation, but introduces both bias and variance. In this work, we fall back to discrete $k$-subset sampling on the forward pass. This is coupled with using the gradient with respect to the exact marginals, computed efficiently, as a proxy for the true gradient. We show that our gradient estimator exhibits lower bias and variance compared to state-of-the-art estimators. Empirical results show improved performance on learning to explain and sparse models benchmarks. We provide an algorithm for computing the exact ELBO for the $k$-subset distribution, obtaining significantly lower loss compared to state-of-the-art discrete sparse VAEs. All of our algorithms are exact and efficient. | https://openreview.net/pdf/0585cf373d23b3b533cd3a3db0e7a229035d92b3.pdf |
Learning Iterative Neural Optimizers for Image Steganography | https://openreview.net/forum?id=gLPkzWjdhBN | https://openreview.net/forum?id=gLPkzWjdhBN | Xiangyu Chen,Varsha Kishore,Kilian Q Weinberger | ICLR 2023,Poster | Image steganography is the process of concealing secret information in images through imperceptible changes.
Recent work has formulated this task as a classic constrained optimization problem. In this paper, we argue that image steganography is inherently performed on the (elusive) manifold of natural images, and propose an iterative neural network trained to perform the optimization steps. In contrast to classical optimization methods like L-BFGS or projected gradient descent, we train the neural network to also stay close to the manifold of natural images throughout the optimization. We show that our learned neural optimization is faster and more reliable than classical optimization approaches. In comparison to previous state-of-the-art encoder-decoder based steganography methods, it reduces the recovery error rate by multiple orders of magnitude and achieves zero error up to 3 bits per pixel (bpp) without the need for error-correcting codes. | https://openreview.net/pdf/df7d87d7a641f922640c4b19b090085abb147920.pdf |
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization | https://openreview.net/forum?id=3aQs3MCSexD | https://openreview.net/forum?id=3aQs3MCSexD | Jonas Geiping,Micah Goldblum,Gowthami Somepalli,Ravid Shwartz-Ziv,Tom Goldstein,Andrew Gordon Wilson | ICLR 2023,Poster | Despite the clear performance benefits of data augmentations, little is known about why they are so effective. In this paper, we disentangle several key mechanisms through which data augmentations operate. Establishing an exchange rate between augmented and additional real data, we find that in out-of-distribution testing scenarios, augmentations which yield samples that are diverse, but inconsistent with the data distribution can be even more valuable than additional training data. Moreover, we find that data augmentations which encourage invariances can be more valuable than invariance alone, especially on small and medium sized training sets. Following this observation, we show that augmentations induce additional stochasticity during training, effectively flattening the loss landscape. | https://openreview.net/pdf/e58b505f873eb3d7715d84faba7dbb45dd3b6295.pdf |
Robust Graph Dictionary Learning | https://openreview.net/forum?id=qxRscesArBZ | https://openreview.net/forum?id=qxRscesArBZ | Weijie Liu,Jiahao Xie,Chao Zhang,Makoto Yamada,Nenggan Zheng,Hui Qian | ICLR 2023,Poster | Traditional Dictionary Learning (DL) aims to approximate data vectors as sparse linear combinations of basis elements (atoms) and is widely used in machine learning, computer vision, and signal processing. To extend DL to graphs, Vincent-Cuaz et al. 2021 propose a method, called GDL, which describes the topology of each graph with a pairwise relation matrix (PRM) and compares PRMs via the Gromov-Wasserstein Discrepancy (GWD). However, the lack of robustness often excludes GDL from a variety of real-world applications since GWD is sensitive to the structural noise in graphs. This paper proposes an improved graph dictionary learning algorithm based on a robust Gromov-Wasserstein discrepancy (RGWD) which has theoretically sound properties and an efficient numerical scheme. Based on such a discrepancy, our dictionary learning algorithm can learn atoms from noisy graph data. Experimental results demonstrate that our algorithm achieves good performance on both simulated and real-world datasets. | https://openreview.net/pdf/56871ede64c94ec08ed1cb85e8f2d77dfab6d20d.pdf |
Fundamental limits on the robustness of image classifiers | https://openreview.net/forum?id=gpmL0D4VjN4 | https://openreview.net/forum?id=gpmL0D4VjN4 | Zheng Dai,David Gifford | ICLR 2023,Poster | We prove that image classifiers are fundamentally sensitive to small perturbations in their inputs. Specifically, we show that given some image space of $n$-by-$n$ images, all but a tiny fraction of images in any image class induced over that space can be moved outside that class by adding some perturbation whose $p$-norm is $O(n^{1/\max{(p,1)}})$, as long as that image class takes up at most half of the image space. We then show that $O(n^{1/\max{(p,1)}})$ is asymptotically optimal. Finally, we show that an increase in the bit depth of the image space leads to a loss in robustness. We supplement our results with a discussion of their implications for vision systems. | https://openreview.net/pdf/df09011b2b400819ffdb954279f6df832a99d466.pdf |
Understanding Influence Functions and Datamodels via Harmonic Analysis | https://openreview.net/forum?id=cxCEOSF99f | https://openreview.net/forum?id=cxCEOSF99f | Nikunj Saunshi,Arushi Gupta,Mark Braverman,Sanjeev Arora | ICLR 2023,Poster | Influence functions estimate effect of individual data points on predictions of the model on test data and were adapted to deep learning in \cite{koh2017understanding}. They have been used for detecting data poisoning, detecting helpful and harmful examples, influence of groups of datapoints, etc. Recently, \cite{ilyas2022datamodels} introduced a linear regression method they termed {\em datamodels} to predict the effect of training points on outputs on test data. The current paper seeks to provide a better theoretical understanding of such interesting empirical phenomena. The primary tool is harmonic analysis and the idea of {\em noise stability}. Contributions include: (a) Exact characterization of the learnt datamodel in terms of Fourier coefficients. (b) An efficient method to estimate the residual error and quality of the optimum linear datamodel without having to train the datamodel. (c) New insights into when influences of groups of datapoints may or may not add up linearly. | https://openreview.net/pdf/aa9ef4d767b4a4bda1b769e885e5a15da0aac3a0.pdf |
TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization | https://openreview.net/forum?id=5tKXUZil3X | https://openreview.net/forum?id=5tKXUZil3X | Bairu Hou,Jinghan Jia,Yihua Zhang,Guanhua Zhang,Yang Zhang,Sijia Liu,Shiyu Chang | ICLR 2023,Poster | Robustness evaluation against adversarial examples has become increasingly important to unveil the trustworthiness of the prevailing deep models in natural language processing (NLP). However, in contrast to the computer vision domain where the first-order projected gradient descent (PGD) is used as the benchmark approach to generate adversarial examples for robustness evaluation, there lacks a principled first-order gradient-based robustness evaluation framework in NLP. The emerging optimization challenges lie in 1) the discrete nature of textual inputs together with the strong coupling between the perturbation location and the actual content, and 2) the additional constraint that the perturbed text should be fluent and achieve a low perplexity under a language model. These challenges make the development of PGD-like NLP attacks difficult. To bridge the gap, we propose TextGrad, a new attack generator using gradient-driven optimization, supporting high-accuracy and high-quality assessment of adversarial robustness in NLP. Specifically, we address the aforementioned challenges in a unified optimization framework. And we develop an effective convex relaxation method to co-optimize the continuously-relaxed site selection and perturbation variables and leverage an effective sampling method to establish an accurate mapping from the continuous optimization variables to the discrete textual perturbations. Moreover, as a first-order attack generation method, TextGrad can be baked into adversarial training to further improve the robustness of NLP models. Extensive experiments are provided to demonstrate the effectiveness of TextGrad not only in attack generation for robustness evaluation but also in adversarial defense. From the attack perspective, we show that TextGrad achieves remarkable improvements in both the attack success rate and the perplexity score over five state-of-the-art baselines. From the defense perspective, TextGrad-enabled adversarial training yields the most robust NLP model against a wide spectrum of NLP attacks. | https://openreview.net/pdf/58c155b8082b3366c06a92d24d638d198650fb53.pdf |
Information Plane Analysis for Dropout Neural Networks | https://openreview.net/forum?id=bQB6qozaBw | https://openreview.net/forum?id=bQB6qozaBw | Linara Adilova,Bernhard C Geiger,Asja Fischer | ICLR 2023,Poster | The information-theoretic framework promises to explain the predictive power of neural networks. In particular, the information plane analysis, which measures mutual information (MI) between input and representation as well as representation and output, should give rich insights into the training process. This approach, however, was shown to strongly depend on the choice of estimator of the MI. The problem is amplified for deterministic networks if the MI between input and representation is infinite. Thus, the estimated values are defined by the different approaches for estimation, but do not adequately represent the training process from an information-theoretic perspective. In this work, we show that dropout with continuously distributed noise ensures that MI is finite. We demonstrate in a range of experiments that this enables a meaningful information plane analysis for a class of dropout neural networks that is widely used in practice. | https://openreview.net/pdf/83ce056c6255f16866c2d52c03f4c82fa2e67366.pdf |
Learning Harmonic Molecular Representations on Riemannian Manifold | https://openreview.net/forum?id=ySCL-NG_I3 | https://openreview.net/forum?id=ySCL-NG_I3 | Yiqun Wang,Yuning Shen,Shi Chen,Lihao Wang,Fei YE,Hao Zhou | ICLR 2023,Poster | Molecular representation learning plays a crucial role in AI-assisted drug discovery research. Encoding 3D molecular structures through Euclidean neural networks has become the prevailing method in the geometric deep learning community. However, the equivariance constraints and message passing in Euclidean space may limit the network expressive power. In this work, we propose a Harmonic Molecular Representation learning (HMR) framework, which represents a molecule using the Laplace-Beltrami eigenfunctions of the molecular surface. HMR offers a multi-resolution representation of molecular geometric and chemical properties on 2D Riemannian manifold. We also introduce a harmonic message passing method to realize efficient spectral message passing over the surface manifold for better molecular encoding. Our proposed method shows comparable predictive power to current models in small molecule property prediction, and outperforms the state-of-the-art deep learning models for the rigid protein docking challenge, demonstrating its versatility in molecular representation learning. | https://openreview.net/pdf/5f6bb5526d4345ce5ea70880325f7ecfccfb1fac.pdf |
Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement | https://openreview.net/forum?id=eSQh8rG8Oa | https://openreview.net/forum?id=eSQh8rG8Oa | Samuel Neumann,Sungsu Lim,Ajin George Joseph,Yangchen Pan,Adam White,Martha White | ICLR 2023,Poster | Many policy gradient methods are variants of Actor-Critic (AC), where a value function (critic) is learned to facilitate updating the parameterized policy (actor). The update to the actor involves a log-likelihood update weighted by the action-values, with the addition of entropy regularization for soft variants. In this work, we explore an alternative update for the actor, based on an extension of the cross entropy method (CEM) to condition on inputs (states). The idea is to start with a broader policy and slowly concentrate around maximal actions, using a maximum likelihood update towards actions in the top percentile per state. The speed of this concentration is controlled by a proposal policy, that concentrates at a slower rate than the actor. We first provide a policy improvement result in an idealized setting, and then prove that our conditional CEM (CCEM) strategy tracks a CEM update per state, even with changing action-values. We empirically show that our Greedy AC algorithm, that uses CCEM for the actor update, performs better than Soft Actor-Critic and is much less sensitive to entropy-regularization. | https://openreview.net/pdf/ffefa87b3379d501de4cdc645c3a08611e72b7ee.pdf |
Efficiently Controlling Multiple Risks with Pareto Testing | https://openreview.net/forum?id=cyg2YXn_BqF | https://openreview.net/forum?id=cyg2YXn_BqF | Bracha Laufer-Goldshtein,Adam Fisch,Regina Barzilay,Tommi S. Jaakkola | ICLR 2023,Poster | Machine learning applications frequently come with multiple diverse objectives and constraints that can change over time. Accordingly, trained models can be tuned with sets of hyper-parameters that affect their predictive behavior (e.g., their run-time efficiency versus error rate). As the number of constraints and hyper-parameter dimensions grow, naively selected settings may lead to sub-optimal and/or unreliable results. We develop an efficient method for calibrating models such that their predictions provably satisfy multiple explicit and simultaneous statistical guarantees (e.g., upper-bounded error rates), while also optimizing any number of additional, unconstrained objectives (e.g., total run-time cost). Building on recent results in distribution-free, finite-sample risk control for general losses, we propose Pareto Testing: a two-stage process which combines multi-objective optimization with multiple hypothesis testing. The optimization stage constructs a set of promising combinations on the Pareto frontier. We then apply statistical testing to this frontier only to identify configurations that have (a) high utility with respect to our objectives, and (b) guaranteed risk levels with respect to our constraints, with specifiably high probability. We demonstrate the effectiveness of our approach to reliably accelerate the execution of large-scale Transformer models in natural language processing (NLP) applications. In particular, we show how Pareto Testing can be used to dynamically configure multiple inter-dependent model attributes—including the number of layers computed before exiting, number of attention heads pruned, or number of text tokens considered—to simultaneously control and optimize various accuracy and cost metrics. | https://openreview.net/pdf/407c53f5fd81796bdf4383718d118a66c0755f95.pdf |
Characteristic Neural Ordinary Differential Equation | https://openreview.net/forum?id=loIfC8WHevK | https://openreview.net/forum?id=loIfC8WHevK | Xingzi Xu,Ali Hasan,Khalil Elkhalil,Jie Ding,Vahid Tarokh | ICLR 2023,Poster | We propose Characteristic-Neural Ordinary Differential Equations (C-NODEs), a framework for extending Neural Ordinary Differential Equations (NODEs) beyond ODEs. While NODE models the evolution of latent variables as the solution to an ODE, C-NODE models the evolution of the latent variables as the solution of a family of first-order partial differential equations (PDEs) along curves on which the PDEs reduce to ODEs, referred to as characteristic curves. This reduction along characteristic curves allows for analyzing PDEs through standard techniques used for ODEs, in particular the adjoint sensitivity method. We also derive C-NODE-based continuous normalizing flows, which describe the density evolution of latent variables along multiple dimensions. Empirical results demonstrate the improvements provided by the proposed method for irregularly sampled time series prediction on MuJoCo, PhysioNet, and Human Activity datasets and classification and density estimation on CIFAR-10, SVHN, and MNIST datasets given a similar computational budget as the existing NODE methods.
The results also provide empirical evidence that the learned curves improve the system efficiency using a lower number of parameters and function evaluations compared with those of the baselines. | https://openreview.net/pdf/58468a45facccea4727809a26664270da375e8d8.pdf |
Fast Sampling of Diffusion Models with Exponential Integrator | https://openreview.net/forum?id=Loek7hfb46P | https://openreview.net/forum?id=Loek7hfb46P | Qinsheng Zhang,Yongxin Chen | ICLR 2023,Poster | The past few years have witnessed the great success of Diffusion models~(DMs) in generating high-fidelity samples in generative modeling tasks. A major limitation of the DM is its notoriously slow sampling procedure which normally requires hundreds to thousands of time discretization steps of the learned diffusion process to reach the desired accuracy. Our goal is to develop a fast sampling method for DMs with a much less number of steps while retaining high sample quality. To this end, we systematically analyze the sampling procedure in DMs and identify key factors that affect the sample quality, among which the method of discretization is most crucial. By carefully examining the learned diffusion process, we propose Diffusion Exponential Integrator Sampler~(DEIS). It is based on the Exponential Integrator designed for discretizing ordinary differential equations (ODEs) and leverages a semilinear structure of the learned diffusion process to reduce the discretization error. The proposed method can be applied to any DMs and can generate high-fidelity samples in as few as 10 steps. Moreover, by directly using pre-trained DMs, we achieve state-of-art sampling performance when the number of score function evaluation~(NFE) is limited, e.g., 4.17 FID with 10 NFEs, 2.86 FID with only 20 NFEs on CIFAR10. | https://openreview.net/pdf/0c36e66f4501ee45a0d7b31d2e7ed21e58c4c628.pdf |
Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation | https://openreview.net/forum?id=A9WQaxYsfx | https://openreview.net/forum?id=A9WQaxYsfx | Hong-Min Chu,Jonas Geiping,Liam H Fowl,Micah Goldblum,Tom Goldstein | ICLR 2023,Poster | As federated learning (FL) matures, privacy attacks against FL systems in turn become more numerous and complex. Attacks on language models have progressed from recovering single sentences in simple classification tasks to recovering larger parts of user data. Current attacks against federated language models are sequence-agnostic and aim to extract as much data as possible from an FL update - often at the expense of fidelity for any particular sequence. Because of this, current attacks fail to extract any meaningful data under large-scale aggregation. In realistic settings, an attacker cares most about a small portion of user data that contains sensitive personal information, for example sequences containing the phrase "my credit card number is ...". In this work, we propose the first attack on FL that achieves targeted extraction of sequences that contain privacy-critical phrases, whereby we employ maliciously modified parameters to allow the transformer itself to filter relevant sequences from aggregated user data and encode them in the gradient update. Our attack can effectively extract sequences of interest even against extremely large-scale aggregation. | https://openreview.net/pdf/62d267bced5f7a24db1b239d5ee670b50f2776cd.pdf |
Artificial Neuronal Ensembles with Learned Context Dependent Gating | https://openreview.net/forum?id=dBk3hsg-n6 | https://openreview.net/forum?id=dBk3hsg-n6 | Matthew James Tilley,Michelle Miller,David Freedman | ICLR 2023,Poster | Biological neural networks are capable of recruiting different sets of neurons to encode different memories. However, when training artificial neural networks on a set of tasks, typically, no mechanism is employed for selectively producing anything analogous to these neuronal ensembles. Further, artificial neural networks suffer from catastrophic forgetting, where the network's performance rapidly deteriorates as tasks are learned sequentially. By contrast, sequential learning is possible for a range of biological organisms. We introduce Learned Context Dependent Gating (LXDG), a method to flexibly allocate and recall `artificial neuronal ensembles', using a particular network structure and a new set of regularization terms. Activities in the hidden layers of the network are modulated by gates, which are dynamically produced during training. The gates are outputs of networks themselves, trained with a sigmoid output activation. The regularization terms we have introduced correspond to properties exhibited by biological neuronal ensembles. The first term penalizes low gate sparsity, ensuring that only a specified fraction of the network is used. The second term ensures that previously learned gates are recalled when the network is presented with input from previously learned tasks. Finally, there is a regularization term responsible for ensuring that new tasks are encoded in gates that are as orthogonal as possible from previously used ones. We demonstrate the ability of this method to alleviate catastrophic forgetting on continual learning benchmarks. When the new regularization terms are included in the model along with Elastic Weight Consolidation (EWC) it achieves better performance on the benchmark `permuted MNIST' than with EWC alone. The benchmark `rotated MNIST' demonstrates how similar tasks recruit similar neurons to the artificial neuronal ensemble. | https://openreview.net/pdf/86c094a695d14e94090e05aae069fe94004b8e07.pdf |
Learning Language Representations with Logical Inductive Bias | https://openreview.net/forum?id=rGeZuBRahju | https://openreview.net/forum?id=rGeZuBRahju | Jianshu Chen | ICLR 2023,Poster | Transformer architectures have achieved great success in solving natural language tasks, which learn strong language representations from large-scale unlabeled texts. In this paper, we seek to go further beyond and explore a new logical inductive bias for better language representation learning. Logic reasoning is known as a formal methodology to reach answers from given knowledge and facts. Inspired by such a view, we develop a novel neural architecture named FOLNet (First-Order Logic Network), to encode this new inductive bias. We construct a set of neural logic operators as learnable Horn clauses, which are further forward-chained into a fully differentiable neural architecture (FOLNet). Interestingly, we find that the self-attention module in transformers can be composed by two of our neural logic operators, which probably explains their strong reasoning performance. Our proposed FOLNet has the same input and output interfaces as other pretrained models and thus could be pretrained/finetuned by using similar losses. It also allows FOLNet to be used in a plug-and-play manner when replacing other pretrained models. With our logical inductive bias, the same set of ``logic deduction skills'' learned through pretraining are expected to be equally capable of solving diverse downstream tasks. For this reason, FOLNet learns language representations that have much stronger transfer capabilities. Experimental results on several language understanding tasks show that our pretrained FOLNet model outperforms the existing strong transformer-based approaches. | https://openreview.net/pdf/6b913f4ba4271068b0c536edce31e0a1d4aebac1.pdf |
How Does Semi-supervised Learning with Pseudo-labelers Work? A Case Study | https://openreview.net/forum?id=Dzmd-Cc8OI | https://openreview.net/forum?id=Dzmd-Cc8OI | Yiwen Kou,Zixiang Chen,Yuan Cao,Quanquan Gu | ICLR 2023,Poster | Semi-supervised learning is a popular machine learning paradigm that utilizes a large amount of unlabeled data as well as a small amount of labeled data to facilitate learning tasks. While semi-supervised learning has achieved great success in training neural networks, its theoretical understanding remains largely open. In this paper, we aim to theoretically understand a semi-supervised learning approach based on pre-training and linear probing. In particular, the semi-supervised learning approach we consider first trains a two-layer neural network based on the unlabeled data with the help of pseudo-labelers. Then it linearly probes the pre-trained network on a small amount of labeled data. We prove that, under a certain toy data generation model and two-layer convolutional neural network, the semisupervised learning approach can achieve nearly zero test loss, while a neural network directly trained by supervised learning on the same amount of labeled data can only achieve constant test loss. Through this case study, we demonstrate a separation between semi-supervised learning and supervised learning in terms of test loss provided the same amount of labeled data. | https://openreview.net/pdf/2e8b65692961f8f668e6c4f616e9634a70af579c.pdf |
Empowering Graph Representation Learning with Test-Time Graph Transformation | https://openreview.net/forum?id=Lnxl5pr018 | https://openreview.net/forum?id=Lnxl5pr018 | Wei Jin,Tong Zhao,Jiayuan Ding,Yozen Liu,Jiliang Tang,Neil Shah | ICLR 2023,Poster | As powerful tools for representation learning on graphs, graph neural networks (GNNs) have facilitated various applications from drug discovery to recommender systems. Nevertheless, the effectiveness of GNNs is immensely challenged by issues related to data quality, such as distribution shift, abnormal features and adversarial attacks. Recent efforts have been made on tackling these issues from a modeling perspective which requires additional cost of changing model architectures or re-training model parameters. In this work, we provide a data-centric view to tackle these issues and propose a graph transformation framework named GTrans which adapts and refines graph data at test time to achieve better performance. We provide theoretical analysis on the design of the framework and discuss why adapting graph data works better than adapting the model. Extensive experiments have demonstrated the effectiveness of GTrans on three distinct scenarios for eight benchmark datasets where suboptimal data is presented. Remarkably, GTrans performs the best in most cases with improvements up to 2.8%, 8.2% and 3.8% over the best baselines on three experimental settings. | https://openreview.net/pdf/84f904a473b1264b64a234dc89199c1ea8d828f9.pdf |
Provable Robustness against Wasserstein Distribution Shifts via Input Randomization | https://openreview.net/forum?id=HJFVrpCaGE | https://openreview.net/forum?id=HJFVrpCaGE | Aounon Kumar,Alexander Levine,Tom Goldstein,Soheil Feizi | ICLR 2023,Poster | Certified robustness in machine learning has primarily focused on adversarial perturbations with a fixed attack budget for each sample in the input distribution. In this work, we present provable robustness guarantees on the accuracy of a model under bounded Wasserstein shifts of the data distribution. We show that a simple procedure that randomizes the input of the model within a transformation space is provably robust to distributional shifts under that transformation. Our framework allows the datum-specific perturbation size to vary across different points in the input distribution and is general enough to include fixed-sized perturbations as well. Our certificates produce guaranteed lower bounds on the performance of the model for any shift (natural or adversarial) of the input distribution within a Wasserstein ball around the original distribution. We apply our technique to certify robustness against natural (non-adversarial) transformations of images such as color shifts, hue shifts, and changes in brightness and saturation. We obtain strong performance guarantees for the robust model under clearly visible shifts in the input images. Our experiments establish the non-vacuousness of our certificates by showing that the certified lower bound on a robust model's accuracy is higher than the empirical accuracy of an undefended model under a distribution shift. Moreover, our results also imply guaranteed lower bounds (hardness result) on the performance of models trained on so-called "unlearnable" datasets that have been poisoned to interfere with model training. We show that the performance of a robust model is guaranteed to remain above a certain threshold on the test distribution even when the base model is trained on the poisoned dataset. | https://openreview.net/pdf/2fb6806a7944ccfc8c512bcb53fd1b093b7abb59.pdf |
Interpretations of Domain Adaptations via Layer Variational Analysis | https://openreview.net/forum?id=YtntjusJV6 | https://openreview.net/forum?id=YtntjusJV6 | Huan-Hsin Tseng,Hsin-Yi Lin,Kuo-Hsuan Hung,Yu Tsao | ICLR 2023,Poster | Transfer learning is known to perform efficiently in many applications empirically, yet limited literature reports the mechanism behind the scene. This study establishes both formal derivations and heuristic analysis to formulate the theory of transfer learning in deep learning. Our framework utilizing layer variational analysis proves that the success of transfer learning can be guaranteed with corresponding data conditions. Moreover, our theoretical calculation yields intuitive interpretations towards the knowledge transfer process. Subsequently, an alternative method for network-based transfer learning is derived. The method shows an increase in efficiency and accuracy for domain adaptation. It is particularly advantageous when new domain data is sufficiently sparse during adaptation. Numerical experiments over diverse tasks validated our theory and verified that our analytic expression achieved better performance in domain adaptation than the gradient descent method. | https://openreview.net/pdf/8887f249acf345628ee7b7c06fe8b3c9b46fb676.pdf |
Denoising Diffusion Samplers | https://openreview.net/forum?id=8pvnfTAbu1f | https://openreview.net/forum?id=8pvnfTAbu1f | Francisco Vargas,Will Sussman Grathwohl,Arnaud Doucet | ICLR 2023,Poster | Denoising diffusion models are a popular class of generative models providing state-of-the-art results in many domains. One adds gradually noise to data using a diffusion to transform the data distribution into a Gaussian distribution. Samples from the generative model are then obtained by simulating an approximation of the time-reversal of this diffusion initialized by Gaussian samples. Practically, the intractable score terms appearing in the time-reversed process are approximated using score matching techniques. We explore here a similar idea to sample approximately from unnormalized probability density functions and estimate their normalizing constants. We consider a process where the target density diffuses towards a Gaussian. Denoising Diffusion Samplers (DDS) are obtained by approximating the corresponding time-reversal. While score matching is not applicable in this context, we can leverage many of the ideas introduced in generative modeling for Monte Carlo sampling. Existing theoretical results from denoising diffusion models also provide theoretical guarantees for DDS. We discuss the connections between DDS, optimal control and Schr\"odinger bridges and finally demonstrate DDS experimentally on a variety of challenging sampling tasks. | https://openreview.net/pdf/0daf337159fe25b03ca3fa62e0e5b86b14cbd4b7.pdf |
How I Learned to Stop Worrying and Love Retraining | https://openreview.net/forum?id=_nF5imFKQI | https://openreview.net/forum?id=_nF5imFKQI | Max Zimmer,Christoph Spiegel,Sebastian Pokutta | ICLR 2023,Poster | Many Neural Network Pruning approaches consist of several iterative training and pruning steps, seemingly losing a significant amount of their performance after pruning and then recovering it in the subsequent retraining phase. Recent works of Renda et al. (2020) and Le & Hua (2021) demonstrate the significance of the learning rate schedule during the retraining phase and propose specific heuristics for choosing such a schedule for IMP (Han et al., 2015). We place these findings in the context of the results of Li et al. (2020) regarding the training of models within a fixed training budget and demonstrate that, consequently, the retraining phase can be massively shortened using a simple linear learning rate schedule. Improving on existing retraining approaches, we additionally propose a method to adaptively select the initial value of the linear schedule. Going a step further, we propose similarly imposing a budget on the initial dense training phase and show that the resulting simple and efficient method is capable of outperforming significantly more complex or heavily parameterized state-of-the-art approaches that attempt to sparsify the network during training. These findings not only advance our understanding of the retraining phase, but more broadly question the belief that one should aim to avoid the need for retraining and reduce the negative effects of ‘hard’ pruning by incorporating the sparsification process into the standard training. | https://openreview.net/pdf/813c852da4cc7b002a8c0c0241eb62d78a910c72.pdf |
Interpretable Geometric Deep Learning via Learnable Randomness Injection | https://openreview.net/forum?id=6u7mf9s2A9 | https://openreview.net/forum?id=6u7mf9s2A9 | Siqi Miao,Yunan Luo,Mia Liu,Pan Li | ICLR 2023,Poster | Point cloud data is ubiquitous in scientific fields. Recently, geometric deep learning (GDL) has been widely applied to solve prediction tasks with such data. However, GDL models are often complicated and hardly interpretable, which poses concerns to scientists who are to deploy these models in scientific analysis and experiments. This work proposes a general mechanism, learnable randomness injection (LRI), which allows building inherently interpretable models based on general GDL backbones. LRI-induced models, once trained, can detect the points in the point cloud data that carry information indicative of the prediction label. We also propose four datasets from real scientific applications that cover the domains of high-energy physics and biochemistry to evaluate the LRI mechanism. Compared with previous post-hoc interpretation methods, the points detected by LRI align much better and stabler with the ground-truth patterns that have actual scientific meanings. LRI is grounded by the information bottleneck principle, and thus LRI-induced models are also more robust to distribution shifts between training and test scenarios. Our code and datasets are available at https://github.com/Graph-COM/LRI. | https://openreview.net/pdf/742515b3aad2ac25c2cc5aae1de6e6df616232e0.pdf |
GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure | https://openreview.net/forum?id=fPVRcJqspu | https://openreview.net/forum?id=fPVRcJqspu | Tennison Liu,Zhaozhi Qian,Jeroen Berrevoets,Mihaela van der Schaar | ICLR 2023,Poster | Deep generative models learn highly complex and non-linear representations to generate realistic synthetic data. While they have achieved notable success in computer vision and natural language processing, similar advances have been less demonstrable in the tabular domain. This is partially because generative modelling of tabular data entails a particular set of challenges, including heterogeneous relationships, limited number of samples, and difficulties in incorporating prior knowledge. Additionally, unlike their counterparts in image and sequence domain, deep generative models for tabular data almost exclusively employ fully-connected layers, which encode weak inductive biases about relationships between inputs. Real-world data generating processes can often be represented using relational structures, which encode sparse, heterogeneous relationships between variables. In this work, we learn and exploit relational structure underlying tabular data to better model variable dependence, and as a natural means to introduce regularization on relationships and include prior knowledge. Specifically, we introduce GOGGLE, an end-to-end message passing scheme that jointly learns the relational structure and corresponding functional relationships as the basis of generating synthetic samples. Using real-world datasets, we provide empirical evidence that the proposed method is effective in generating realistic synthetic data and exploiting domain knowledge for downstream tasks. | https://openreview.net/pdf/7589a804d2686c2acfc5aa8c679329619789df08.pdf |
Progressive Prompts: Continual Learning for Language Models | https://openreview.net/forum?id=UJTgQBc91_ | https://openreview.net/forum?id=UJTgQBc91_ | Anastasia Razdaibiedina,Yuning Mao,Rui Hou,Madian Khabsa,Mike Lewis,Amjad Almahairi | ICLR 2023,Poster | We introduce Progressive Prompts – a simple and efficient approach for continual learning in language models. Our method allows forward transfer and resists catastrophic forgetting, without relying on data replay or a large number of task-specific parameters. Progressive Prompts learns a new soft prompt for each task and sequentially concatenates it with the previously learned prompts, while keeping the base model frozen. Experiments on standard continual learning benchmarks show that our approach outperforms state-of-the-art methods, with an improvement >20% in average test accuracy over the previous best-preforming method on T5 model. We also explore a more challenging continual learning setup with longer sequences of tasks and show that Progressive Prompts significantly outperforms prior methods. | https://openreview.net/pdf/fd24677a6ed80ba3b6c8dbbcacfbeb6bbd507bc5.pdf |
Deep Learning From Crowdsourced Labels: Coupled Cross-Entropy Minimization, Identifiability, and Regularization | https://openreview.net/forum?id=_qVhsWyWB9 | https://openreview.net/forum?id=_qVhsWyWB9 | Shahana Ibrahim,Tri Nguyen,Xiao Fu | ICLR 2023,Poster | Using noisy crowdsourced labels from multiple annotators, a deep learning-based end-to-end (E2E) system aims to learn the label correction mechanism and the neural classifier simultaneously. To this end, many E2E systems concatenate the neural classifier with multiple annotator-specific label confusion layers and co-train the two parts in a parameter-coupled manner. The formulated coupled cross-entropy minimization (CCEM)-type criteria are intuitive and work well in practice. Nonetheless, theoretical understanding of the CCEM criterion has been limited. The contribution of this work is twofold: First, performance guarantees of the CCEM criterion are presented. Our analysis reveals for the first time that the CCEM can indeed correctly identify the annotators' confusion characteristics and the desired ``ground-truth'' neural classifier under realistic conditions, e.g., when only incomplete annotator labeling and finite samples are available. Second, based on the insights learned from our analysis, two regularized variants of the CCEM are proposed. The regularization terms provably enhance the identifiability of the target model parameters in various more challenging cases. A series of synthetic and real data experiments are presented to showcase the effectiveness of our approach. | https://openreview.net/pdf/49ef61c9a86ec546eaf7789c562a056aff1ad491.pdf |
Projective Proximal Gradient Descent for Nonconvex Nonsmooth Optimization: Fast Convergence Without Kurdyka-Lojasiewicz (KL) Property | https://openreview.net/forum?id=yEsj8pGNl1 | https://openreview.net/forum?id=yEsj8pGNl1 | Yingzhen Yang,Ping Li | ICLR 2023,Poster | Nonconvex and nonsmooth optimization problems are important and challenging for statistics and machine learning. In this paper, we propose Projected Proximal Gradient Descent (PPGD) which solves a class of nonconvex and nonsmooth optimization problems, where the nonconvexity and nonsmoothness come from a nonsmooth regularization term which is nonconvex but piecewise convex. In contrast with existing convergence analysis of accelerated PGD methods for nonconvex and nonsmooth problems based on the Kurdyka-\L{}ojasiewicz (K\L{}) property, we provide a new theoretical analysis showing local fast convergence of PPGD. It is proved that PPGD achieves a fast convergence rate of $O(1/k^2)$ when the iteration number $k \ge k_0$ for a finite $k_0$ on a class of nonconvex and nonsmooth problems under mild assumptions, which is locally the Nesterov's optimal convergence rate of first-order methods on smooth and convex objective function with Lipschitz continuous gradient. Experimental results demonstrate the effectiveness of PPGD. | https://openreview.net/pdf/2450c95bdb425cd07ead03d14845ff5b1f23b0be.pdf |
First Steps Toward Understanding the Extrapolation of Nonlinear Models to Unseen Domains | https://openreview.net/forum?id=7wrq3vHcMM | https://openreview.net/forum?id=7wrq3vHcMM | Kefan Dong,Tengyu Ma | ICLR 2023,Poster | Real-world machine learning applications often involve deploying neural networks to domains that are not seen in the training time. Hence, we need to understand the extrapolation of \textit{nonlinear} models---under what conditions on the distributions and function class, models can be guaranteed to extrapolate to new test distributions. The question is very challenging because even two-layer neural networks cannot be guaranteed to extrapolate outside the support of the training distribution without further assumptions on the domain shift. This paper makes some initial steps towards analyzing the extrapolation of nonlinear models for structured domain shift. We primarily consider settings where the \textit{marginal} distribution of each coordinate of the data (or subset of coordinates) do not shift significantly across the training and test distributions, but the joint distribution may have a much bigger shift. We prove that the family of nonlinear models of the form $f(x)=\sum f_i(x_i)$, where $f_i$ is an \emph{arbitrary} function on the subset of features $x_i$, can extrapolate to unseen distributions, if the covariance of the features is well-conditioned. To the best of our knowledge, this is the first result that goes beyond linear models and the bounded density ratio assumption, even though the assumptions on the distribution shift and function class are stylized. | https://openreview.net/pdf/b2cc4ce52139e02a517c71f6e985807c9c33ef4a.pdf |
Compositionality with Variation Reliably Emerges in Neural Networks | https://openreview.net/forum?id=-Yzz6vlX7V- | https://openreview.net/forum?id=-Yzz6vlX7V- | Henry Conklin,Kenny Smith | ICLR 2023,Poster | Human languages enable robust generalization, letting us leverage our prior experience to communicate about novel meanings. This is partly due to language being compositional, where the meaning of a whole expression is a function of its parts. Natural languages also exhibit extensive variation, encoding meaning predictably enough to enable generalization without limiting speakers to one and only one way of expressing something. Previous work looking at the languages that emerge between neural networks in a communicative task has shown languages that enable robust communication and generalization reliably emerge. Despite this those languages score poorly on existing measures of compositionality leading to claims that a language's degree of compositionality has little bearing on how well it can generalise. We argue that the languages that emerge between networks are in fact straightforwardly compositional, but with a degree of natural language-like variation that can obscure their compositionality from existing measures. We introduce 4 measures of linguistic variation and show that early in training measures of variation correlate with generalization performance, but that this effect goes away over time as the languages that emerge become regular enough to generalize robustly. Like natural languages, emergent languages appear able to support a high degree of variation while retaining the generalizability we expect from compositionality. In an effort to decrease the variability of emergent languages we show how reducing a model's capacity results in greater regularity, in line with claims about factors shaping the emergence of regularity in human language. | https://openreview.net/pdf/a8f063cfdec6854e3c3536574f1512244e9cbf8c.pdf |
Systematic Rectification of Language Models via Dead-end Analysis | https://openreview.net/forum?id=k8_yVW3Wqln | https://openreview.net/forum?id=k8_yVW3Wqln | Meng Cao,Mehdi Fatemi,Jackie CK Cheung,Samira Shabanian | ICLR 2023,Poster | With adversarial or otherwise normal prompts, existing large language models (LLM) can be pushed to generate toxic discourses. One way to reduce the risk of LLMs generating undesired discourses is to alter the training of the LLM. This can be very restrictive due to demanding computation requirements. Other methods rely on rule-based or prompt-based token elimination, which are limited as they dismiss future tokens and the overall meaning of the complete discourse. Here, we center detoxification on the probability that the finished discourse is ultimately considered toxic. That is, at each point, we advise against token selections proportional to how likely a finished text from this point will be toxic. To this end, we formally extend the dead-end theory from the recent reinforcement learning (RL) literature to also cover uncertain outcomes. Our approach, called rectification, utilizes a separate but significantly smaller model for detoxification, which can be applied to diverse LLMs as long as they share the same vocabulary. Importantly, our method does not require access to the internal representations of the LLM, but only the token probability distribution at each decoding step. We believe this is important since many LLMs today are hosted in servers and only accessible through APIs. When applied to various LLMs, including GPT-3, our approach generates notably better results compared to the base LLMs and other techniques in terms of the overall language and detoxification performance. | https://openreview.net/pdf/04f652450ee4706ff6565dc9df8580e1881050b2.pdf |
Multiple sequence alignment as a sequence-to-sequence learning problem | https://openreview.net/forum?id=8efJYMBrNb | https://openreview.net/forum?id=8efJYMBrNb | Edo Dotan,Yonatan Belinkov,Oren Avram,Elya Wygoda,Noa Ecker,Michael Alburquerque,Omri Keren,Gil Loewenthal,Tal Pupko | ICLR 2023,Poster | The sequence alignment problem is one of the most fundamental problems in bioinformatics and a plethora of methods were devised to tackle it. Here we introduce BetaAlign, a methodology for aligning sequences using an NLP approach. BetaAlign accounts for the possible variability of the evolutionary process among different datasets by using an ensemble of transformers, each trained on millions of samples generated from a different evolutionary model. Our approach leads to alignment accuracy that is similar and often better than commonly used methods, such as MAFFT, DIALIGN, ClustalW, T-Coffee, PRANK, and MUSCLE. | https://openreview.net/pdf/24f63d3c1ae4f4aabbced2cb7f592b1352507e4c.pdf |
A Mixture-of-Expert Approach to RL-based Dialogue Management | https://openreview.net/forum?id=4FBUihxz5nm | https://openreview.net/forum?id=4FBUihxz5nm | Yinlam Chow,Azamat Tulepbergenov,Ofir Nachum,Dhawal Gupta,Moonkyung Ryu,Mohammad Ghavamzadeh,Craig Boutilier | ICLR 2023,Poster | Despite recent advancements in language models (LMs), their application to dialogue management (DM) problems and ability to carry on rich conversations remain a challenge. We use reinforcement learning (RL) to develop a dialogue agent that avoids being short-sighted (outputting generic utterances) and maximizes overall user satisfaction. Most existing RL approaches to DM train the agent at the word-level, and thus, have to deal with a combinatorially complex action space even for a medium-size vocabulary. As a result, they struggle to produce a successful and engaging dialogue even if they are warm-started with a pre-trained LM. To address this issue, we develop a RL-based DM using a novel mixture of expert language model (MoE-LM) that consists of (i) a LM capable of learning diverse semantics for conversation histories, (ii) a number of specialized LMs (or experts) capable of generating utterances corresponding to a particular attribute or personality, and (iii) a RL-based DM that performs dialogue planning with the utterances generated by the experts. Our MoE approach provides greater flexibility to generate sensible utterances with different intents and allows RL to focus on conversational-level DM. We compare it with SOTA baselines on open-domain dialogues and demonstrate its effectiveness both in terms of the diversity and sensibility of the generated utterances and the overall DM performance. | https://openreview.net/pdf/ed30d6917d0c6d712d5a89e6216da216ad910d4d.pdf |
f-DM: A Multi-stage Diffusion Model via Progressive Signal Transformation | https://openreview.net/forum?id=iBdwKIsg4m | https://openreview.net/forum?id=iBdwKIsg4m | Jiatao Gu,Shuangfei Zhai,Yizhe Zhang,Miguel Ángel Bautista,Joshua M. Susskind | ICLR 2023,Poster | Diffusion models (DMs) have recently emerged as SoTA tools for generative modeling in various domains. Standard DMs can be viewed as an instantiation of hierarchical variational autoencoders (VAEs) where the latent variables are inferred from input-centered Gaussian distributions with fixed scales and variances. Unlike VAEs, this formulation constrains DMs from changing the latent spaces and
learning abstract representations. In this work, we propose f-DM, a generalized family of DMs which allows progressive signal transformation. More precisely, we extend DMs to incorporate a set of (hand-designed or learned) transformations, where the transformed input is the mean of each diffusion step. We propose a generalized formulation and derive the corresponding de-noising objective with a modified sampling algorithm. As a demonstration, we apply f-DM in image generation tasks with a range of functions, including down-sampling, blurring, and learned transformations based on the encoder of pretrained VAEs. In addition, we identify the importance of adjusting the noise levels whenever the signal is sub-sampled and propose a simple rescaling recipe. f-DM can produce high-quality samples on standard image generation benchmarks like FFHQ, AFHQ, LSUN, and ImageNet with better efficiency and semantic interpretation. | https://openreview.net/pdf/b7219af3a9c6c0c0add8ae9fa74bae7fd3cc27f6.pdf |
Backpropagation at the Infinitesimal Inference Limit of Energy-Based Models: Unifying Predictive Coding, Equilibrium Propagation, and Contrastive Hebbian Learning | https://openreview.net/forum?id=nIMifqu2EO | https://openreview.net/forum?id=nIMifqu2EO | Beren Millidge,Yuhang Song,Tommaso Salvatori,Thomas Lukasiewicz,Rafal Bogacz | ICLR 2023,Poster | How the brain performs credit assignment is a fundamental unsolved problem in neuroscience. Many `biologically plausible' algorithms have been proposed, which compute gradients that approximate those computed by backpropagation (BP), and which operate in ways that more closely satisfy the constraints imposed by neural circuitry. Many such algorithms utilize the framework of energy-based models (EBMs), in which all free variables in the model are optimized to minimize a global energy function. However, in the literature, these algorithms exist in isolation and no unified theory exists linking them together. Here, we provide a comprehensive theory of the conditions under which EBMs can approximate BP, which lets us unify many of the BP approximation results in the literature (namely, predictive coding, equilibrium propagation, and contrastive Hebbian learning) and demonstrate that their approximation to BP arises from a simple and general mathematical property of EBMs at free-phase equilibrium. This property can then be exploited in different ways with different energy functions, and these specific choices yield a family of BP-approximating algorithms, which both includes the known results in the literature and can be used to derive new ones. | https://openreview.net/pdf/7373fa1c70e3207165e7c6b8b5ea7e4cc16c46a8.pdf |
A Theoretical Framework for Inference and Learning in Predictive Coding Networks | https://openreview.net/forum?id=ZCTvSF_uVM4 | https://openreview.net/forum?id=ZCTvSF_uVM4 | Beren Millidge,Yuhang Song,Tommaso Salvatori,Thomas Lukasiewicz,Rafal Bogacz | ICLR 2023,Poster | Predictive coding (PC) is an influential theory in computational neuroscience, which argues that the cortex forms unsupervised world models by implementing a hierarchical process of prediction error minimization. PC networks (PCNs) are trained in two phases. First, neural activities are updated to optimize the network's response to external stimuli. Second, synaptic weights are updated to consolidate this change in activity --- an algorithm called \emph{prospective configuration}. While previous work has shown how in various limits, PCNs can be found to approximate backpropagation (BP), recent work has demonstrated that PCNs operating in this standard regime, which does not approximate BP, nevertheless obtain competitive training and generalization performance to BP-trained networks while outperforming them on various tasks. However, little is understood theoretically about the properties and dynamics of PCNs in this regime. In this paper, we provide a comprehensive theoretical analysis of the properties of PCNs trained with prospective configuration. We first derive analytical results concerning the inference equilibrium for PCNs and a previously unknown close connection relationship to target propagation (TP). Secondly, we provide a theoretical analysis of learning in PCNs as a variant of generalized expectation-maximization and use that to prove the convergence of PCNs to critical points of the BP loss function, thus showing that deep PCNs can, in theory, achieve the same generalization performance as BP, while maintaining their unique advantages. | https://openreview.net/pdf/15d048b2a93e6a13bade281774bb0e064b51237c.pdf |
The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes | https://openreview.net/forum?id=JLINxPOVTh7 | https://openreview.net/forum?id=JLINxPOVTh7 | Alexander Atanasov,Blake Bordelon,Sabarish Sainathan,Cengiz Pehlevan | ICLR 2023,Poster | For small training set sizes $P$, the generalization error of wide neural networks is well-approximated by the error of an infinite width neural network (NN), either in the kernel or mean-field/feature-learning regime. However, after a critical sample size $P^*$, we empirically find the finite-width network generalization becomes worse than that of the infinite width network. In this work, we empirically study the transition from infinite-width behavior to this \textit{variance-limited} regime as a function of sample size $P$ and network width $N$. We find that finite-size effects can become relevant for very small dataset sizes on the order of $P^* \sim \sqrt{N}$ for polynomial regression with ReLU networks. We discuss the source of these effects using an argument based on the variance of the NN's final neural tangent kernel (NTK). This transition can be pushed to larger $P$ by enhancing feature learning or by ensemble averaging the networks. We find that the learning curve for regression with the final NTK is an accurate approximation of the NN learning curve. Using this, we provide a toy model which also exhibits $P^* \sim \sqrt{N}$ scaling and has $P$-dependent benefits from feature learning. | https://openreview.net/pdf/de1e91b8ac0ef2549a351ca9178f69bfec816cc2.pdf |
A Simple Approach for Visual Room Rearrangement: 3D Mapping and Semantic Search | https://openreview.net/forum?id=1C6nCCaRe6p | https://openreview.net/forum?id=1C6nCCaRe6p | Brandon Trabucco,Gunnar A Sigurdsson,Robinson Piramuthu,Gaurav S. Sukhatme,Ruslan Salakhutdinov | ICLR 2023,Poster | Physically rearranging objects is an important capability for embodied agents. Visual room rearrangement evaluates an agent's ability to rearrange objects in a room to a desired goal based solely on visual input. We propose a simple yet effective method for this problem: (1) search for and map which objects need to be rearranged, and (2) rearrange each object until the task is complete. Our approach consists of an off-the-shelf semantic segmentation model, voxel-based semantic map, and semantic search policy to efficiently find objects that need to be rearranged. Our method was the winning submission to the AI2-THOR Rearrangement Challenge in the 2022 Embodied AI Workshop at CVPR 2022, and improves on current state-of-the-art end-to-end reinforcement learning-based methods that learn visual room rearrangement policies from 0.53% correct rearrangement to 16.56%, using only 2.7% as many samples from the environment. | https://openreview.net/pdf/da9e28116ad9e608e30a012bb8a930a488fd5ff0.pdf |
Progressive Mix-Up for Few-Shot Supervised Multi-Source Domain Transfer | https://openreview.net/forum?id=H7M_5K5qKJV | https://openreview.net/forum?id=H7M_5K5qKJV | Ronghang Zhu,Ronghang Zhu,Xiang Yu,Sheng Li | ICLR 2023,Poster | This paper targets at a new and challenging setting of knowledge transfer from multiple source domains to a single target domain, where target data is few shot or even one shot with label. Traditional domain generalization or adaptation methods cannot directly work since there is no sufficient target domain distribution serving as the transfer object. The multi-source setting further prevents the transfer task as excessive domain gap introduced from all the source domains. To tackle this problem, we newly propose a progressive mix-up (P-Mixup) mechanism to introduce an intermediate mix-up domain, pushing both the source domains and the few-shot target domain aligned to this mix-up domain. Further by enforcing the mix-up domain to progressively move towards the source domains, we achieve the domain transfer from multi-source domains to the single one-shot target domain. Our P-Mixup is different from traditional mix-up that ours is with a progressive and adaptive mix-up ratio, following the curriculum learning spirit to better align the source and target domains. Moreover, our P-Mixup combines both pixel-level and feature-level mix-up to better enrich the data diversity. Experiments on two benchmarks show that our P-Mixup significantly outperforms the state-of-the-art methods, i.e., 6.0\% and 6.8\% improvements on Office-Home and DomainNet. | https://openreview.net/pdf/9e54132af1c66891c78ab803b81ba0fbbccdfc8b.pdf |
Neural Compositional Rule Learning for Knowledge Graph Reasoning | https://openreview.net/forum?id=F8VKQyDgRVj | https://openreview.net/forum?id=F8VKQyDgRVj | Kewei Cheng,Nesreen Ahmed,Yizhou Sun | ICLR 2023,Poster | Learning logical rules is critical to improving reasoning in KGs. This is due to their ability to provide logical and interpretable explanations when used for predictions, as well as their ability to generalize to other tasks, domains, and data. While recent methods have been proposed to learn logical rules, the majority of these methods are either restricted by their computational complexity and can not handle the large search space of large-scale KGs, or show poor generalization when exposed to data outside the training set. In this paper, we propose an end-to-end neural model for learning compositional logical rules called NCRL. NCRL detects the best compositional structure of a rule body, and breaks it into small compositions in order to infer the rule head. By recurrently merging compositions in the rule body with a recurrent attention unit, NCRL finally predicts a single rule head. Experimental results show that NCRL learns high-quality rules, as well as being generalizable. Specifically, we show that NCRL is scalable, efficient, and yields state-of-the-art results for knowledge graph completion on large-scale KGs. Moreover, we test NCRL for systematic generalization by learning to reason on small-scale observed graphs and evaluating on larger unseen ones. | https://openreview.net/pdf/b901536a159ad120243e968ea0448b44b6ae3850.pdf |
Efficient approximation of neural population structure and correlations with probabilistic circuits | https://openreview.net/forum?id=XC_yGI-0j9 | https://openreview.net/forum?id=XC_yGI-0j9 | Koosha Khalvati,Samantha Johnson,Stefan Mihalas,Michael A Buice | ICLR 2023,Poster | We present a computationally efficient framework to model a wide range of population structures with high order correlations and a large number of neurons. Our method is based on a special type of Bayesian network that has linear inference time and is founded upon the concept of contextual independence. Moreover, we use an efficient architecture learning method for network selection to model large neural populations even with a small amount of data. Our framework is both fast and accurate in approximating neural population structures. Furthermore, our approach enables us to reliably quantify higher order neural correlations. We test our method on simulated neural populations commonly used to generate higher order correlations, as well as on publicly available large-scale neural recordings from the Allen Brain Observatory. Our approach significantly outperforms other models both in terms of statistical measures and alignment with experimental evidence. | https://openreview.net/pdf/d84305a77e8a3f43d7ddcd8e3abd46e8e8051e0d.pdf |
Exploring perceptual straightness in learned visual representations | https://openreview.net/forum?id=4cOfD2qL6T | https://openreview.net/forum?id=4cOfD2qL6T | Anne Harrington,Vasha DuTell,Ayush Tewari,Mark Hamilton,Simon Stent,Ruth Rosenholtz,William T. Freeman | ICLR 2023,Poster | Humans have been shown to use a ''straightened'' encoding to represent the natural visual world as it evolves in time (Henaff et al. 2019). In the context of discrete video sequences, ''straightened'' means that changes between frames follow a more linear path in representation space at progressively deeper levels of processing. While deep convolutional networks are often proposed as models of human visual processing, many do not straighten natural videos. In this paper, we explore the relationship between network architecture, differing types of robustness, biologically-inspired filtering mechanisms, and representational straightness in response to time-varying input; we identify strengths and limitations of straightness as a useful way of evaluating neural network representations. We find that (1) adversarial training leads to straighter representations in both CNN and transformer-based architectures but (2) this effect is task-dependent, not generalizing to tasks such as segmentation and frame-prediction, where straight representations are not favorable for predictions; and nor to other types of robustness. In addition, (3) straighter representations impart temporal stability to class predictions, even for out-of-distribution data. Finally, (4) biologically-inspired elements increase straightness in the early stages of a network, but do not guarantee increased straightness in downstream layers of CNNs. We show that straightness is an easily computed measure of representational robustness and stability, as well as a hallmark of human representations with benefits for computer vision models. | https://openreview.net/pdf/123b8a992a9e420ee592c1f8b175c9a439aa2f58.pdf |
Is Forgetting Less a Good Inductive Bias for Forward Transfer? | https://openreview.net/forum?id=dL35lx-mTEs | https://openreview.net/forum?id=dL35lx-mTEs | Jiefeng Chen,Timothy Nguyen,Dilan Gorur,Arslan Chaudhry | ICLR 2023,Poster | One of the main motivations of studying continual learning is that the problem setting allows a model to accrue knowledge from past tasks to learn new tasks more efficiently. However, recent studies suggest that the key metric that continual learning algorithms optimize, reduction in catastrophic forgetting, does not correlate well with the forward transfer of knowledge. We believe that the conclusion previous works reached is due to the way they measure forward transfer. We argue that the measure of forward transfer to a task should not be affected by the restrictions placed on the continual learner in order to preserve knowledge of previous tasks. Instead, forward transfer should be measured by how easy it is to learn a new task given a set of representations produced by continual learning on previous tasks. Under this notion of forward transfer, we evaluate different continual learning algorithms on a variety of image classification benchmarks. Our results indicate that less forgetful representations lead to a better forward transfer suggesting a strong correlation between retaining past information and learning efficiency on new tasks. Further, we found less forgetful representations to be more diverse and discriminative compared to their forgetful counterparts. | https://openreview.net/pdf/e98c841e4d915d736af993e7cece2a394298133a.pdf |
Learning Structured Representations by Embedding Class Hierarchy | https://openreview.net/forum?id=7J-30ilaUZM | https://openreview.net/forum?id=7J-30ilaUZM | Siqi Zeng,Remi Tachet des Combes,Han Zhao | ICLR 2023,Poster | Existing models for learning representations in supervised classification problems are permutation invariant with respect to class labels. However, structured knowledge about the classes, such as hierarchical label structures, widely exists in many real-world datasets, e.g., the ImageNet and CIFAR benchmarks. How to learn representations that can preserve such structures among the classes remains an open problem. To approach this problem, given a tree of class hierarchy, we first define a tree metric between any pair of nodes in the tree to be the length of the shortest path connecting them. We then provide a method to learn the hierarchical relationship of class labels by approximately embedding the tree metric in the Euclidean space of features. More concretely, during supervised training, we propose to use the Cophenetic Correlation Coefficient (CPCC) as a regularizer for the cross-entropy loss to correlate the tree metric of classes and the Euclidean distance in the class-conditioned representations. Our proposed regularizer is computationally lightweight and easy to implement. Empirically, we demonstrate that this approach can help to learn more interpretable representations due to the preservation of the tree metric, and leads to better in-distribution generalization as well as under sub-population shifts over six real-world datasets. | https://openreview.net/pdf/415e4e3c2bf538db1f22fe01a021e0e56edef7b9.pdf |
Promptagator: Few-shot Dense Retrieval From 8 Examples | https://openreview.net/forum?id=gmL46YMpu2J | https://openreview.net/forum?id=gmL46YMpu2J | Zhuyun Dai,Vincent Y Zhao,Ji Ma,Yi Luan,Jianmo Ni,Jing Lu,Anton Bakalov,Kelvin Guu,Keith Hall,Ming-Wei Chang | ICLR 2023,Poster | Much recent research on information retrieval has focused on how to transfer from one task (typically with abundant supervised data) to various other retrieval tasks where supervision is limited, with the implicit assumption that it is possible to generalize from one task to all the rest. However, this overlooks the fact that there are many diverse and unique retrieval problems, each targeting different search intents, queries, and search domains. In this paper, we suggest to work on Few-shot Dense Retrieval, a setting where each task comes with a short description and a few examples. To address this, we introduce Prompt-based Query Generation forRetrieval (Promptagator): for each task, we feed the few-shot examples to a large language model (LLM) and prompt it to behave as a task-specific query generator. Using this, we can synthetically generate a large number of relevant queries for any document, yielding abundant data for training task-specific retrievers --- with no reliance on traditional resources such as Natural Questions (Kwiatkowskiet al., 2019) or MS MARCO (Nguyen et al., 2016). Surprisingly, Promptagator with only 8 annotated examples enables efficient dual encoder retrievers to outperform computationally more expensive models trained on MS MARCO such as ColBERT v2 (Santhanam et al., 2022) by more than 1.2 points nDCG@10 on average on 11 retrieval sets. Further training standard-size re-rankers using the same generated data yields another 5.0 points nDCG@10 improvement. Our studies show that synthetic query generation can be far more effective than previously observed, especially when a small amount of task-specific knowledge is given. | https://openreview.net/pdf/79a0f9b78ef87a8465c2f60eac8f96b996c84b38.pdf |
Brain-like representational straightening of natural movies in robust feedforward neural networks | https://openreview.net/forum?id=mCmerkTCG2S | https://openreview.net/forum?id=mCmerkTCG2S | Tahereh Toosi,Elias Issa | ICLR 2023,Poster | Representational straightening refers to a decrease in curvature of visual feature representations of a sequence of frames taken from natural movies. Prior work established straightening in neural representations of the primate primary visual cortex (V1) and perceptual straightening in human behavior as a hallmark of biological vision in contrast to artificial feedforward neural networks which did not demonstrate this phenomenon as they were not explicitly optimized to produce temporally predictable movie representations. Here, we show robustness to noise in the input image can produce representational straightening in feedforward neural networks. Both adversarial training (AT) and base classifiers for Random Smoothing (RS) induced remarkably straightened feature codes. Demonstrating their utility within the domain of natural movies, these codes could be inverted to generate intervening movie frames by linear interpolation in the feature space even though they were not trained on these trajectories. Demonstrating their biological utility, we found that AT and RS training improved predictions of neural data in primate V1 over baseline models providing a parsimonious, bio-plausible mechanism -- noise in the sensory input stages -- for generating representations in early visual cortex. Finally, we compared the geometric properties of frame representations in these networks to better understand how they produced representations that mimicked the straightening phenomenon from biology. Overall, this work elucidating emergent properties of robust neural networks demonstrates that it is not necessary to utilize predictive objectives or train directly on natural movie statistics to achieve models supporting straightened movie representations similar to human perception that also predict V1 neural responses.
| https://openreview.net/pdf/25778f3dab7cc7e64ceaf20c080374ea579c9e78.pdf |
FunkNN: Neural Interpolation for Functional Generation | https://openreview.net/forum?id=BT4N_v7CLrk | https://openreview.net/forum?id=BT4N_v7CLrk | AmirEhsan Khorashadizadeh,Anadi Chaman,Valentin Debarnot,Ivan Dokmanić | ICLR 2023,Poster | Can we build continuous generative models which generalize across scales, can be evaluated at any coordinate, admit calculation of exact derivatives, and are conceptually simple? Existing MLP-based architectures generate worse samples than the grid-based generators with favorable convolutional inductive biases. Models that focus on generating images at different scales do better, but employ complex architectures not designed for continuous evaluation of images and derivatives.
We take a signal-processing perspective and treat continuous signal generation as interpolation from samples. Indeed, correctly sampled discrete images contain all information about the low spatial frequencies. The question is then how to extrapolate the spectrum in a data-driven way while meeting the above design criteria. Our answer is FunkNN---a novel convolutional network which learns how to reconstruct continuous images at arbitrary coordinates and can be applied to any image dataset. Combined with a discrete generative model it becomes a functional generator which can act as a prior in continuous ill-posed inverse problems. We show that FunkNN generates high-quality continuous images and exhibits strong out-of-distribution performance thanks to its patch-based design. We further showcase its performance in several stylized inverse problems with exact spatial derivatives. | https://openreview.net/pdf/28092c32b86cad94aa628631b9fbee14db96b208.pdf |
Label Propagation with Weak Supervision | https://openreview.net/forum?id=aCuFa-RRqtI | https://openreview.net/forum?id=aCuFa-RRqtI | Rattana Pukdee,Dylan Sam,Pradeep Kumar Ravikumar,Nina Balcan | ICLR 2023,Poster | Semi-supervised learning and weakly supervised learning are important paradigms that aim to reduce the growing demand for labeled data in current machine learning applications. In this paper, we introduce a novel analysis of the classical label propagation algorithm (LPA) (Zhu & Ghahramani, 2002) that moreover takes advantage of useful prior information, specifically probabilistic hypothesized labels on the unlabeled data. We provide an error bound that exploits both the local geometric properties of the underlying graph and the quality of the prior information. We also propose a framework to incorporate multiple sources of noisy information. In particular, we consider the setting of weak supervision, where our sources of information are weak labelers. We demonstrate the ability of our approach on multiple benchmark weakly supervised classification tasks, showing improvements upon existing semi-supervised and weakly supervised methods. | https://openreview.net/pdf/f5eb9084b11c20ac8c1192c548ad3a24221c2768.pdf |
TypeT5: Seq2seq Type Inference using Static Analysis | https://openreview.net/forum?id=4TyNEhI2GdN | https://openreview.net/forum?id=4TyNEhI2GdN | Jiayi Wei,Greg Durrett,Isil Dillig | ICLR 2023,Poster | There has been growing interest in automatically predicting missing type annotations in programs written in Python and JavaScript. While prior methods have achieved impressive accuracy when predicting the most common types, they often perform poorly on rare or complex types. In this paper, we present a new type inference method that treats type prediction as a code infilling task by leveraging CodeT5, a state-of-the-art seq2seq pre-trained language model for code. Our method uses static analysis to construct dynamic contexts for each code element whose type signature is to be predicted by the model. We also propose an iterative decoding scheme that incorporates previous type predictions in the model's input context, allowing information exchange between related code elements. Our evaluation shows that the proposed approach, TypeT5, not only achieves a higher overall accuracy (particularly on rare and complex types) but also produces more coherent results with fewer type errors---while enabling easy user intervention. | https://openreview.net/pdf/1db193cae16df420c7376f835dbc310fd7c3d31b.pdf |
AGRO: Adversarial discovery of error-prone Groups for Robust Optimization | https://openreview.net/forum?id=IrzkT99fDJH | https://openreview.net/forum?id=IrzkT99fDJH | Bhargavi Paranjape,Pradeep Dasigi,Vivek Srikumar,Luke Zettlemoyer,Hannaneh Hajishirzi | ICLR 2023,Poster | Models trained via empirical risk minimization (ERM) are known to rely on spurious correlations between labels and task-independent input features, resulting in poor generalization to distributional shifts. Group distributionally robust optimization (G-DRO) can alleviate this problem by minimizing the worst-case loss over a set of pre-defined groups over training data. G-DRO successfully improves performance of the worst group, where the correlation does not hold. However, G-DRO assumes that the spurious correlations and associated worst groups are known in advance, making it challenging to apply them to new tasks with potentially multiple unknown correlations. We propose AGRO---Adversarial Group discovery for Distributionally Robust Optimization---an end-to-end approach that jointly identifies error-prone groups and improves accuracy on them. AGRO equips G-DRO with an adversarial slicing model to find a group assignment for training examples which maximizes worst-case loss over the discovered groups. On the WILDS benchmark, AGRO results in 8\% higher model performance on average on known worst-groups, compared to prior group discovery approaches used with G-DRO. AGRO also improves out-of-distribution performance on SST2, QQP, and MS-COCO---datasets where potential spurious correlations are as yet uncharacterized. Human evaluation of ARGO groups shows that they contain well-defined, yet previously unstudied spurious correlations that lead to model errors. | https://openreview.net/pdf/917c28995eb2cfb8dba1865414692a0c7cf1e345.pdf |
LogicDP: Creating Labels for Graph Data via Inductive Logic Programming | https://openreview.net/forum?id=2b2s9vd7wYv | https://openreview.net/forum?id=2b2s9vd7wYv | Yuan Yang,Faramarz Fekri,James Clayton Kerce,Ali Payani | ICLR 2023,Poster | Graph data, such as scene graphs and knowledge graphs, see wide use in AI systems. In real-world and large applications graph data are usually incomplete, motivating graph reasoning models for missing-fact or missing-relationship inference. While these models can achieve state-of-the-art performance, they require a large amount of training data.
Recent years have witnessed the rising interest in label creation with data programming (DP) methods, which aim to generate training labels from heuristic labeling functions. However, existing methods typically focus on unstructured data and are not optimized for graphs. In this work, we propose LogicDP, a data programming framework for graph data. Unlike existing DP methods, (1) LogicDP utilizes the inductive logic programming (ILP) technique and automatically discovers the labeling functions from the graph data; (2) LogicDP employs a budget-aware framework to iteratively refine the functions by querying an oracle, which significantly reduces the human efforts in function creations. Experiments show that LogicDP achieves better data efficiency in both scene graph and knowledge graph reasoning tasks. | https://openreview.net/pdf/7f0e71c89583a4faa2321873243855b581f2bdf7.pdf |
Revisiting Intrinsic Reward for Exploration in Procedurally Generated Environments | https://openreview.net/forum?id=j3GK3_xZydY | https://openreview.net/forum?id=j3GK3_xZydY | Kaixin Wang,Kuangqi Zhou,Bingyi Kang,Jiashi Feng,Shuicheng YAN | ICLR 2023,Poster | Exploration under sparse rewards remains a key challenge in deep reinforcement learning. Recently, studying exploration in procedurally-generated environments has drawn increasing attention. Existing works generally combine lifelong intrinsic rewards and episodic intrinsic rewards to encourage exploration. Though various lifelong and episodic intrinsic rewards have been proposed, the individual contributions of the two kinds of intrinsic rewards to improving exploration are barely investigated. To bridge this gap, we disentangle these two parts and conduct ablative experiments. We consider lifelong and episodic intrinsic rewards used in prior works, and compare the performance of all lifelong-episodic combinations on the commonly used MiniGrid benchmark. Experimental results show that only using episodic intrinsic rewards can match or surpass prior state-of-the-art methods. On the other hand, only using lifelong intrinsic rewards hardly makes progress in exploration. This demonstrates that episodic intrinsic reward is more crucial than lifelong one in boosting exploration. Moreover, we find through experimental analysis that the lifelong intrinsic reward does not accurately reflect the novelty of states, which explains why it does not help much in improving exploration. | https://openreview.net/pdf/5b63f069d20506723467e420a5a7a64916ab8335.pdf |
Transformer-based World Models Are Happy With 100k Interactions | https://openreview.net/forum?id=TdBaDGCpjly | https://openreview.net/forum?id=TdBaDGCpjly | Jan Robine,Marc Höftmann,Tobias Uelwer,Stefan Harmeling | ICLR 2023,Poster | Deep neural networks have been successful in many reinforcement learning settings. However, compared to human learners they are overly data hungry. To build a sample-efficient world model, we apply a transformer to real-world episodes in an autoregressive manner: not only the compact latent states and the taken actions but also the experienced or predicted rewards are fed into the transformer, so that it can attend flexibly to all three modalities at different time steps. The transformer allows our world model to access previous states directly, instead of viewing them through a compressed recurrent state. By utilizing the Transformer-XL architecture, it is able to learn long-term dependencies while staying computationally efficient. Our transformer-based world model (TWM) generates meaningful, new experience, which is used to train a policy that outperforms previous model-free and model-based reinforcement learning algorithms on the Atari 100k benchmark. Our code is available at https://github.com/jrobine/twm. | https://openreview.net/pdf/7399276efc2d9eb8ae74a790e054008f752b84b5.pdf |
Can Neural Networks Learn Implicit Logic from Physical Reasoning? | https://openreview.net/forum?id=HVoJCRLByVk | https://openreview.net/forum?id=HVoJCRLByVk | Aaron Traylor,Roman Feiman,Ellie Pavlick | ICLR 2023,Poster | Despite the success of neural network models in a range of domains, it remains an open question whether they can learn to represent abstract logical operators such as negation and disjunction. We test the hypothesis that neural networks without inherent inductive biases for logical reasoning can acquire an implicit representation of negation and disjunction. Here, implicit refers to limited, domain-specific forms of these operators, and work in psychology suggests these operators may be a precursor (developmentally and evolutionarily) to the type of abstract, domain-general logic that is characteristic of adult humans. To test neural networks, we adapt a test designed to diagnose the presence of negation and disjunction in animals and pre-verbal children, which requires inferring the location of a hidden object using constraints of the physical environment as well as implicit logic: if a ball is hidden in A or B, and shown not to be in A, can the subject infer that it is in B? Our results show that, despite the neural networks learning to track objects behind occlusion, they are unable to generalize to a task that requires implicit logic. We further show that models are unable to generalize to the test task even when they are trained directly on a logically identical (though visually dissimilar) task. However, experiments using transfer learning reveal that the models do recognize structural similarity between tasks which invoke the same logical reasoning pattern, suggesting that some desirable abstractions are learned, even if they are not yet sufficient to pass established tests of logical reasoning. | https://openreview.net/pdf/c5c2b2a5deae5ea8590411d5949116224affc655.pdf |
ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret | https://openreview.net/forum?id=35QyoZv8cKO | https://openreview.net/forum?id=35QyoZv8cKO | Stephen Marcus McAleer,Gabriele Farina,Marc Lanctot,Tuomas Sandholm | ICLR 2023,Poster | Recent techniques for approximating Nash equilibria in very large games leverage neural networks to learn approximately optimal policies (strategies). One promis- ing line of research uses neural networks to approximate counterfactual regret minimization (CFR) or its modern variants. DREAM, the only current CFR-based neural method that is model free and therefore scalable to very large games, trains a neural network on an estimated regret target that can have extremely high variance due to an importance sampling term inherited from Monte Carlo CFR (MCCFR). In this paper we propose an unbiased model-free method that does not require any importance sampling. Our method, ESCHER, is principled and is guaranteed to converge to an approximate Nash equilibrium with high probability. We show that the variance of the estimated regret of ESCHER is orders of magnitude lower than DREAM and other baselines. We then show that ESCHER outperforms the prior state of the art—DREAM and neural fictitious self play (NFSP)—on a number of games and the difference becomes dramatic as game size increases. In the very large game of dark chess, ESCHER is able to beat DREAM and NFSP in a head-to-head competition over 90% of the time. | https://openreview.net/pdf/cdb7c9b1ffde6e7acae887c13857a09a87b3d158.pdf |
On Achieving Optimal Adversarial Test Error | https://openreview.net/forum?id=fVm3nZMZs9 | https://openreview.net/forum?id=fVm3nZMZs9 | Justin D. Li,Matus Telgarsky | ICLR 2023,Poster | We first elucidate various fundamental properties of optimal adversarial predictors: the structure of optimal adversarial convex predictors in terms of optimal adversarial zero-one predictors, bounds relating the adversarial convex loss to the adversarial zero-one loss, and the fact that continuous predictors can get arbitrarily close to the optimal adversarial error for both convex and zero-one losses. Applying these results along with new Rademacher complexity bounds for adversarial training near initialization, we prove that for general data distributions and perturbation sets, adversarial training on shallow networks with early stopping and an idealized optimal adversary is able to achieve optimal adversarial test error. By contrast, prior theoretical work either considered specialized data distributions or only provided training error guarantees. | https://openreview.net/pdf/a08af08ee33be325cb0f141b73e0caeb271917be.pdf |
Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation | https://openreview.net/forum?id=FJXf1FXN8C | https://openreview.net/forum?id=FJXf1FXN8C | Jun-Kun Wang,Andre Wibisono | ICLR 2023,Poster | We consider a setting that a model needs to adapt to a new domain under distribution shifts, given that only unlabeled test samples from the new domain are accessible at test time. A common idea in most of the related works is constructing pseudo-labels for the unlabeled test samples and applying gradient descent (GD) to a loss function with the pseudo-labels. Recently, Goyal et al. (2022) propose conjugate labels, which is a new kind of pseudo-labels for self-training at test time. They empirically show that the conjugate label outperforms other ways of pseudo-labeling on many domain adaptation benchmarks. However, provably showing that GD with conjugate labels learns a good classifier for test-time adaptation remains open. In this work, we aim at theoretically understanding GD with hard and conjugate labels for a binary classification problem. We show that for square loss, GD with conjugate labels converges to an $\epsilon$-optimal predictor under a Gaussian model for any arbitrarily small $\epsilon$, while GD with hard pseudo-labels fails in this task. We also analyze them under different loss functions for the update. Our results shed lights on understanding when and why GD with hard labels or conjugate labels works in test-time adaptation. | https://openreview.net/pdf/7d000edac11267f0b5a613d835780cd4ccb9102d.pdf |
A VAE for Transformers with Nonparametric Variational Information Bottleneck | https://openreview.net/forum?id=6QkjC_cs03X | https://openreview.net/forum?id=6QkjC_cs03X | James Henderson,Fabio James Fehr | ICLR 2023,Poster | We propose a Variational AutoEncoder (VAE) for Transformers by developing a Variational Information Bottleneck (VIB) regulariser for Transformer embeddings. We formalise such attention-based representations as mixture distributions, and use Bayesian nonparametrics to develop a Nonparametric VIB (NVIB) for them. The variable number of mixture components supported by nonparametrics captures the variable number of vectors supported by attention, and exchangeable distributions from nonparametrics capture the permutation invariance of attention. Our Transformer VAE (NVAE) uses NVIB to regularise the information passing from the Transformer encoder to the Transformer decoder. Evaluations of a NVAE, trained on natural language text, demonstrate that NVIB can regularise the number of mixture components in the induced embedding whilst maintaining generation quality and reconstruction capacity. | https://openreview.net/pdf/a0427f590a1b2bdae48d9d5bf012e33be0c7837c.pdf |
On The Specialization of Neural Modules | https://openreview.net/forum?id=Fh97BDaR6I | https://openreview.net/forum?id=Fh97BDaR6I | Devon Jarvis,Richard Klein,Benjamin Rosman,Andrew M Saxe | ICLR 2023,Poster | A number of machine learning models have been proposed with the goal of achieving systematic generalization: the ability to reason about new situations by combining aspects of previous experiences. These models leverage compositional architectures which aim to learn specialized modules dedicated to structures in a task that can be composed to solve novel problems with similar structures. While the compositionality of these architectures is guaranteed by design, the modules specializing is not. Here we theoretically study the ability of network modules to specialize to useful structures in a dataset and achieve systematic generalization. To this end we introduce a minimal space of datasets motivated by practical systematic generalization benchmarks. From this space of datasets we present a mathematical definition of systematicity and study the learning dynamics of linear neural modules when solving components of the task. Our results shed light on the difficulty of module specialization, what is required for modules to successfully specialize, and the necessity of modular architectures to achieve systematicity. Finally, we confirm that the theoretical results in our tractable setting generalize to more complex datasets and non-linear architectures. | https://openreview.net/pdf/77b2b9320e912b9c57129834fe4eafdfeaf9399f.pdf |
HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers | https://openreview.net/forum?id=D7srTrGhAs | https://openreview.net/forum?id=D7srTrGhAs | Chen Liang,Haoming Jiang,Zheng Li,Xianfeng Tang,Bing Yin,Tuo Zhao | ICLR 2023,Poster | Knowledge distillation has been shown to be a powerful model compression approach to facilitate the deployment of pre-trained language models in practice. This paper focuses on task-agnostic distillation. It produces a compact pre-trained model that can be easily fine-tuned on various tasks with small computational costs and memory footprints. Despite the practical benefits, task-agnostic distillation is challenging. Since the teacher model has a significantly larger capacity and stronger representation power than the student model, it is very difficult for the student to produce predictions that match the teacher's over a massive amount of open-domain training data. Such a large prediction discrepancy often diminishes the benefits of knowledge distillation. To address this challenge, we propose Homotopic Distillation (HomoDistil), a novel task-agnostic distillation approach equipped with iterative pruning. Specifically, we initialize the student model from the teacher model, and iteratively prune the student's neurons until the target width is reached. Such an approach maintains a small discrepancy between the teacher's and student's predictions throughout the distillation process, which ensures the effectiveness of knowledge transfer. Extensive experiments demonstrate that HomoDistil achieves significant improvements on existing baselines. Our codes will be released. | https://openreview.net/pdf/c83a0d7736988f2fb7bb42f283697dade74b84f2.pdf |
Using Both Demonstrations and Language Instructions to Efficiently Learn Robotic Tasks | https://openreview.net/forum?id=4u42KCQxCn8 | https://openreview.net/forum?id=4u42KCQxCn8 | Albert Yu,Ray Mooney | ICLR 2023,Poster | Demonstrations and natural language instructions are two common ways to specify and teach robots novel tasks. However, for many complex tasks, a demonstration or language instruction alone contains ambiguities, preventing tasks from being specified clearly. In such cases, a combination of both a demonstration and an instruction more concisely and effectively conveys the task to the robot than either modality alone. To instantiate this problem setting, we train a single multi-task policy on a few hundred challenging robotic pick-and-place tasks and propose DeL-TaCo (Joint Demo-Language Task Conditioning), a method for conditioning a robotic policy on task embeddings comprised of two components: a visual demonstration and a language instruction. By allowing these two modalities to mutually disambiguate and clarify each other during novel task specification, DeL-TaCo (1) substantially decreases the teacher effort needed to specify a new task and (2) achieves better generalization performance on novel objects and instructions over previous task-conditioning methods. To our knowledge, this is the first work to show that simultaneously conditioning a multi-task robotic manipulation policy on both demonstration and language embeddings improves sample efficiency and generalization over conditioning on either modality alone. | https://openreview.net/pdf/737207c4212f43218eda90063019985cab18548e.pdf |
FIGARO: Controllable Music Generation using Learned and Expert Features | https://openreview.net/forum?id=NyR8OZFHw6i | https://openreview.net/forum?id=NyR8OZFHw6i | Dimitri von Rütte,Luca Biggio,Yannic Kilcher,Thomas Hofmann | ICLR 2023,Poster | Recent symbolic music generative models have achieved significant improvements in the quality of the generated samples. Nevertheless, it remains hard for users to control the output in such a way that it matches their expectation. To address this limitation, high-level, human-interpretable conditioning is essential. In this work, we release FIGARO, a Transformer-based conditional model trained to generate symbolic music based on a sequence of high-level control codes. To this end, we propose description-to-sequence learning, which consists of automatically extracting fine-grained, human-interpretable features (the description) and training a sequence-to-sequence model to reconstruct the original sequence given only the description as input. FIGARO achieves state-of-the-art performance in multi-track symbolic music generation both in terms of style transfer and sample quality. We show that performance can be further improved by combining human-interpretable with learned features. Our extensive experimental evaluation shows that FIGARO is able to generate samples that closely adhere to the content of the input descriptions, even when they deviate significantly from the training distribution. | https://openreview.net/pdf/4ee95daea73cb05d2ea8780258b25684ccd82a88.pdf |