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Control Variates for Slate Off-Policy Evaluation


Jun 15, 2021
Nikos Vlassis, Ashok Chandrashekar, Fernando Amat Gil, Nathan Kallus


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Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning


Jun 03, 2021
Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou, Nathan Kallus, Mark van der Laan


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Post-Contextual-Bandit Inference


Jun 01, 2021
Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou, Nathan Kallus, Mark van der Laan


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Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach


Mar 29, 2021
Nathan Kallus, Xiaojie Mao, Masatoshi Uehara


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Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency


Feb 05, 2021
Masatoshi Uehara, Masaaki Imaizumi, Nan Jiang, Nathan Kallus, Wen Sun, Tengyang Xie

* Under Review 

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Fast Rates for the Regret of Offline Reinforcement Learning


Jan 31, 2021
Yichun Hu, Nathan Kallus, Masatoshi Uehara


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Fairness, Welfare, and Equity in Personalized Pricing


Dec 27, 2020
Nathan Kallus, Angela Zhou

* Accepted at FAccT 2021 

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The Variational Method of Moments


Dec 17, 2020
Andrew Bennett, Nathan Kallus


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Rejoinder: New Objectives for Policy Learning


Dec 05, 2020
Nathan Kallus


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Fast Rates for Contextual Linear Optimization


Nov 05, 2020
Yichun Hu, Nathan Kallus, Xiaojie Mao


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Optimal Off-Policy Evaluation from Multiple Logging Policies


Oct 21, 2020
Nathan Kallus, Yuta Saito, Masatoshi Uehara

* Under Review 

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Stochastic Optimization Forests


Sep 08, 2020
Nathan Kallus, Xiaojie Mao


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Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders


Jul 27, 2020
Andrew Bennett, Nathan Kallus, Lihong Li, Ali Mousavi


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Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies


Jun 06, 2020
Nathan Kallus, Masatoshi Uehara


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Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning


Jun 06, 2020
Nathan Kallus, Masatoshi Uehara


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DTR Bandit: Learning to Make Response-Adaptive Decisions With Low Regret


Jun 05, 2020
Yichun Hu, Nathan Kallus


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On the Optimality of Randomization in Experimental Design: How to Randomize for Minimax Variance and Design-Based Inference


May 06, 2020
Nathan Kallus


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Comment: Entropy Learning for Dynamic Treatment Regimes


Apr 06, 2020
Nathan Kallus

* Statistica Sinica 29.4 (2019): 1697-1705 

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On the role of surrogates in the efficient estimation of treatment effects with limited outcome data


Mar 27, 2020
Nathan Kallus, Xiaojie Mao


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Statistically Efficient Off-Policy Policy Gradients


Feb 20, 2020
Nathan Kallus, Masatoshi Uehara


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Efficient Policy Learning from Surrogate-Loss Classification Reductions


Feb 12, 2020
Andrew Bennett, Nathan Kallus


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Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning


Feb 11, 2020
Nathan Kallus, Angela Zhou


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Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects


Jan 21, 2020
Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David Sontag


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Localized Debiased Machine Learning: Efficient Estimation of Quantile Treatment Effects, Conditional Value at Risk, and Beyond


Dec 30, 2019
Nathan Kallus, Xiaojie Mao, Masatoshi Uehara


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Kernel Optimal Orthogonality Weighting: A Balancing Approach to Estimating Effects of Continuous Treatments


Oct 26, 2019
Nathan Kallus, Michele Santacatterina


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