Deep Learning for Functional Data Analysis with Adaptive Basis Layers Paper • 2106.10414 • Published Jun 19, 2021
Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features Paper • 2110.13413 • Published Oct 26, 2021
A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features Paper • 2206.08473 • Published Jun 16, 2022
Time-Varying Propensity Score to Bridge the Gap between the Past and Present Paper • 2210.01422 • Published Oct 4, 2022
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data Paper • 2003.06505 • Published Mar 13, 2020
CROWDLAB: Supervised learning to infer consensus labels and quality scores for data with multiple annotators Paper • 2210.06812 • Published Oct 13, 2022
Identifying Incorrect Annotations in Multi-Label Classification Data Paper • 2211.13895 • Published Nov 25, 2022
ActiveLab: Active Learning with Re-Labeling by Multiple Annotators Paper • 2301.11856 • Published Jan 27, 2023
Detecting Dataset Drift and Non-IID Sampling via k-Nearest Neighbors Paper • 2305.15696 • Published May 25, 2023
Detecting Errors in a Numerical Response via any Regression Model Paper • 2305.16583 • Published May 26, 2023
Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness Paper • 2308.16175 • Published Aug 30, 2023
Educating Text Autoencoders: Latent Representation Guidance via Denoising Paper • 1905.12777 • Published May 29, 2019
Benchmarking Multimodal AutoML for Tabular Data with Text Fields Paper • 2111.02705 • Published Nov 4, 2021
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing Paper • 2109.11105 • Published Sep 23, 2021
Automated Data Curation for Robust Language Model Fine-Tuning Paper • 2403.12776 • Published Mar 19, 2024
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks Paper • 2103.14749 • Published Mar 26, 2021