Niladri Shekhar Chatterji — accepted AI-conference papers

2022

Is Importance Weighting Incompatible with Interpolating Classifiers?

Ke Alexander Wang, Niladri Shekhar Chatterji, Saminul Haque, Tatsunori Hashimoto

ICLR 2022poster

Importance weighting is a classic technique to handle distribution shifts. However, prior work has presented strong empirical and theoretical evidence demonstrating that importance weights can have little to no effect on overparameterized neural networks. \emph{Is importance weighting truly incompat…

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2021

On the Theory of Reinforcement Learning with Once-per-Episode Feedback

Niladri Shekhar Chatterji, Aldo Pacchiano, Peter Bartlett, Michael Jordan

NeurIPS 2021poster

We study a theory of reinforcement learning (RL) in which the learner receives binary feedback only once at the end of an episode. While this is an extreme test case for theory, it is also arguably more representative of real-world applications than the traditional requirement in RL practice that th…

Cited by 40 Source ↗PDF ↗Scholar ↗