226 citations · 1.9k across the 128 of their papers we have counts for
12 papers · 1 filter
Why do classifier accuracies show linear trends under distribution shift?
Horia Mania, Suvrit Sra
Recent studies of generalization in deep learning have observed a puzzling trend: accuracies of models on one data distribution are approximately linear functions of the accuracies…
Online Learning in Unknown Markov Games
Yi Tian, Yuanhao Wang, Tiancheng Yu +1
We study online learning in unknown Markov games, a problem that arises in episodic multi-agent reinforcement learning where the actions of the opponents are unobservable. We show…
Coping with Label Shift via Distributionally Robust Optimisation
Jingzhao Zhang, Aditya Menon, Andreas Veit +3
The label shift problem refers to the supervised learning setting where the train and test label distributions do not match. Existing work addressing label shift usually assumes ac…
Contrastive Learning with Hard Negative Samples
Joshua Robinson, Ching-Yao Chuang, Suvrit Sra +1
How can you sample good negative examples for contrastive learning? We argue that, as with metric learning, contrastive learning of representations benefits from hard negative samp…
Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes
Yi Tian, Jian Qian, Suvrit Sra
We study minimax optimal reinforcement learning in episodic factored Markov decision processes (FMDPs), which are MDPs with conditionally independent transition components. Assumin…
SGD with shuffling: optimal rates without component convexity and large epoch requirements
Kwangjun Ahn, Chulhee Yun, Suvrit Sra
We study without-replacement SGD for solving finite-sum optimization problems. Specifically, depending on how the indices of the finite-sum are shuffled, we consider the RandomShuf…