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Nathan Kallus

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Hessian-Free Laplace in Bayesian Deep Learning

Mar 15, 2024
James McInerney, Nathan Kallus

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Switching the Loss Reduces the Cost in Batch Reinforcement Learning

Mar 12, 2024
Alex Ayoub, Kaiwen Wang, Vincent Liu, Samuel Robertson, James McInerney, Dawen Liang, Nathan Kallus, Csaba Szepesvári

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Risk-Sensitive RL with Optimized Certainty Equivalents via Reduction to Standard RL

Mar 10, 2024
Kaiwen Wang, Dawen Liang, Nathan Kallus, Wen Sun

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Is Cosine-Similarity of Embeddings Really About Similarity?

Mar 08, 2024
Harald Steck, Chaitanya Ekanadham, Nathan Kallus

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Applied Causal Inference Powered by ML and AI

Mar 04, 2024
Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, Vasilis Syrgkanis

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

Feb 16, 2024
Brian Cho, Kyra Gan, 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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Multi-Armed Bandits with Interference

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

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