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Nathan Kallus

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More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning

Feb 11, 2024
Kaiwen Wang, Owen Oertell, Alekh Agarwal, Nathan Kallus, Wen Sun

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Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams

Feb 09, 2024
Brian Cho, Kyra Gan, Nathan Kallus

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Multi-Armed Bandits with Interference

Feb 02, 2024
Su Jia, Peter Frazier, Nathan Kallus

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Faster Rates for Switchback Experiments

Dec 25, 2023
Su Jia, Sohom Bhattacharya, Nathan Kallus, Christina Lee Yu

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Low-Rank MDPs with Continuous Action Spaces

Nov 06, 2023
Andrew Bennett, Nathan Kallus, Miruna Oprescu

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Off-Policy Evaluation for Large Action Spaces via Policy Convolution

Oct 24, 2023
Noveen Sachdeva, Lequn Wang, Dawen Liang, Nathan Kallus, Julian McAuley

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Large Language Models as Zero-Shot Conversational Recommenders

Aug 19, 2023
Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, Julian McAuley

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Source Condition Double Robust Inference on Functionals of Inverse Problems

Jul 25, 2023
Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara

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JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning

Jul 21, 2023
Kaiwen Wang, Junxiong Wang, Yueying Li, Nathan Kallus, Immanuel Trummer, Wen Sun

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The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning

May 25, 2023
Kaiwen Wang, Kevin Zhou, Runzhe Wu, Nathan Kallus, Wen Sun

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