166 citations · 595 across the 71 of their papers we have counts for
5 papers · 2 filters
PAC Prediction Sets Under Label Shift
Wenwen Si, Sangdon Park, Insup Lee +2
Prediction sets capture uncertainty by predicting sets of labels rather than individual labels, enabling downstream decisions to conservatively account for all plausible outcomes.…
A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks
Behrad Moniri, Donghwan Lee, Hamed Hassani +1
Feature learning is thought to be one of the fundamental reasons for the success of deep neural networks. It is rigorously known that in two-layer fully-connected neural networks u…
Minimax Statistical Estimation under Wasserstein Contamination
Patrick Chao, Edgar Dobriban
Contaminations are a key concern in modern statistical learning, as small but systematic perturbations of all datapoints can substantially alter estimation results. Here, we study…
Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneity
Xinmeng Huang, Kan Xu, Donghwan Lee +3
Large and complex datasets are often collected from several, possibly heterogeneous sources. Multitask learning methods improve efficiency by leveraging commonalities across datase…
Demystifying Disagreement-on-the-Line in High Dimensions
Donghwan Lee, Behrad Moniri, Xinmeng Huang +2
Evaluating the performance of machine learning models under distribution shift is challenging, especially when we only have unlabeled data from the shifted (target) domain, along w…