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2020 – today
- 2023
[j15]Nathan Kallus
, Xiaojie Mao:
Stochastic Optimization Forests. Manag. Sci. 69(4): 1975-1994 (2023)- [c52]Nathan Kallus, Miruna Oprescu:
Robust and Agnostic Learning of Conditional Distributional Treatment Effects. AISTATS 2023: 6037-6060 - [c51]Andrew Bennett, Dipendra Misra, Nathan Kallus:
Provable Safe Reinforcement Learning with Binary Feedback. AISTATS 2023: 10871-10900 - [c50]Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara:
Inference on Strongly Identified Functionals of Weakly Identified Functions. COLT 2023: 2265 - [c49]Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara:
Minimax Instrumental Variable Regression and L2 Convergence Guarantees without Identification or Closedness. COLT 2023: 2291-2318 - [c48]Su Jia, Qian Xie, Nathan Kallus, Peter I. Frazier:
Smooth Non-stationary Bandits. ICML 2023: 14930-14944 - [c47]Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit:
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding. ICML 2023: 26599-26618 - [c46]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. ICML 2023: 34615-34641 - [c45]Kaiwen Wang, Nathan Kallus, Wen Sun:
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR. ICML 2023: 35864-35907 - [i72]Su Jia, Qian Xie, Nathan Kallus, Peter I. Frazier:
Smooth Non-Stationary Bandits. CoRR abs/2301.12366 (2023) - [i71]Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Refined Value-Based Offline RL under Realizability and Partial Coverage. CoRR abs/2302.02392 (2023) - [i70]Kaiwen Wang, Nathan Kallus, Wen Sun:
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR. CoRR abs/2302.03201 (2023) - [i69]Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara:
Minimax Instrumental Variable Regression and $L_2$ Convergence Guarantees without Identification or Closedness. CoRR abs/2302.05404 (2023) - [i68]Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit:
B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding. CoRR abs/2304.10577 (2023) - [i67]Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Provable Offline Reinforcement Learning with Human Feedback. CoRR abs/2305.14816 (2023) - [i66]Kaiwen Wang, Kevin Zhou, Runzhe Wu, Nathan Kallus, Wen Sun:
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning. CoRR abs/2305.15703 (2023) - [i65]Kaiwen Wang, Junxiong Wang, Yueying Li, Nathan Kallus, Immanuel Trummer, Wen Sun:
JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning. CoRR abs/2307.11704 (2023) - [i64]Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara:
Source Condition Double Robust Inference on Functionals of Inverse Problems. CoRR abs/2307.13793 (2023) - [i63]Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, Julian J. McAuley:
Large Language Models as Zero-Shot Conversational Recommenders. CoRR abs/2308.10053 (2023) - 2022
- [j14]Yichun Hu, Nathan Kallus, Xiaojie Mao:
Smooth Contextual Bandits: Bridging the Parametric and Nondifferentiable Regret Regimes. Oper. Res. 70(6): 3261-3281 (2022) - [j13]Nathan Kallus, Masatoshi Uehara:
Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning. Oper. Res. 70(6): 3282-3302 (2022) - [j12]Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David A. Sontag:
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects. J. Mach. Learn. Res. 23: 166:1-166:50 (2022) - [j11]Vishal Gupta, Nathan Kallus:
Data Pooling in Stochastic Optimization. Manag. Sci. 68(3): 1595-1615 (2022) - [j10]Nathan Kallus, Xiaojie Mao, Angela Zhou:
Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination. Manag. Sci. 68(3): 1959-1981 (2022) - [j9]Yichun Hu, Nathan Kallus, Xiaojie Mao:
Fast Rates for Contextual Linear Optimization. Manag. Sci. 68(6): 4236-4245 (2022) - [c44]Nathan Kallus, Angela Zhou:
Stateful Offline Contextual Policy Evaluation and Learning. AISTATS 2022: 11169-11194 - [c43]Shervin Ardeshir, Cristina Segalin, Nathan Kallus:
Estimating Structural Disparities for Face Models. CVPR 2022: 10348-10357 - [c42]Nathan Kallus:
Treatment Effect Risk: Bounds and Inference. FAccT 2022: 213 - [c41]Jonathan D. Chang, Kaiwen Wang, Nathan Kallus, Wen Sun:
Learning Bellman Complete Representations for Offline Policy Evaluation. ICML 2022: 2938-2971 - [c40]Nathan Kallus, Xiaojie Mao, Kaiwen Wang, Zhengyuan Zhou:
Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning. ICML 2022: 10598-10632 - [c39]Nathan Kallus:
What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment. NeurIPS 2022 - [c38]Nathan Kallus, James McInerney:
The Implicit Delta Method. NeurIPS 2022 - [c37]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. NeurIPS 2022 - [i62]Nathan Kallus, Xiaojie Mao, Kaiwen Wang, Zhengyuan Zhou:
Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning. CoRR abs/2202.09667 (2022) - [i61]Shervin Ardeshir, Cristina Segalin, Nathan Kallus:
Estimating Structural Disparities for Face Models. CoRR abs/2204.06562 (2022) - [i60]Nathan Kallus:
What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment. CoRR abs/2205.10327 (2022) - [i59]Nathan Kallus, Miruna Oprescu:
Robust and Agnostic Learning of Conditional Distributional Treatment Effects. CoRR abs/2205.11486 (2022) - [i58]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. CoRR abs/2206.12020 (2022) - [i57]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. CoRR abs/2206.12081 (2022) - [i56]Jonathan D. Chang, Kaiwen Wang, Nathan Kallus, Wen Sun:
Learning Bellman Complete Representations for Offline Policy Evaluation. CoRR abs/2207.05837 (2022) - [i55]Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang, Nathan Kallus, Chengchun Shi, Wen Sun:
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. CoRR abs/2207.13081 (2022) - [i54]Andrew Bennett, Dipendra Misra, Nathan Kallus:
Provable Safe Reinforcement Learning with Binary Feedback. CoRR abs/2210.14492 (2022) - [i53]Nathan Kallus, James McInerney:
The Implicit Delta Method. CoRR abs/2211.06457 (2022) - [i52]Masatoshi Uehara, Chengchun Shi, Nathan Kallus:
A Review of Off-Policy Evaluation in Reinforcement Learning. CoRR abs/2212.06355 (2022) - 2021
- [j8]Nathan Kallus, Angela Zhou:
Minimax-Optimal Policy Learning Under Unobserved Confounding. Manag. Sci. 67(5): 2870-2890 (2021) - [c36]Andrew Bennett, Nathan Kallus, Lihong Li, Ali Mousavi:
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders. AISTATS 2021: 1999-2007 - [c35]Yichun Hu, Nathan Kallus, Masatoshi Uehara:
Fast Rates for the Regret of Offline Reinforcement Learning. COLT 2021: 2462 - [c34]Nathan Kallus, Angela Zhou:
Fairness, Welfare, and Equity in Personalized Pricing. FAccT 2021: 296-314 - [c33]Nathan Kallus, Yuta Saito, Masatoshi Uehara:
Optimal Off-Policy Evaluation from Multiple Logging Policies. ICML 2021: 5247-5256 - [c32]Nikos Vlassis, Ashok Chandrashekar, Fernando Amat Gil, Nathan Kallus:
Control Variates for Slate Off-Policy Evaluation. NeurIPS 2021: 3667-3679 - [c31]Aurélien Bibaut, Nathan Kallus, Maria Dimakopoulou, Antoine Chambaz, Mark J. van der Laan:
Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning. NeurIPS 2021: 19261-19273 - [c30]Aurélien Bibaut, Maria Dimakopoulou, Nathan Kallus, Antoine Chambaz, Mark J. van der Laan:
Post-Contextual-Bandit Inference. NeurIPS 2021: 28548-28559 - [i51]Yichun Hu, Nathan Kallus, Masatoshi Uehara:
Fast Rates for the Regret of Offline Reinforcement Learning. CoRR abs/2102.00479 (2021) - [i50]Masatoshi Uehara, Masaaki Imaizumi, Nan Jiang, Nathan Kallus, Wen Sun, Tengyang Xie:
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency. CoRR abs/2102.02981 (2021) - [i49]Nathan Kallus, Xiaojie Mao, Masatoshi Uehara:
Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach. CoRR abs/2103.14029 (2021) - [i48]Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou, Nathan Kallus, Mark J. van der Laan:
Post-Contextual-Bandit Inference. CoRR abs/2106.00418 (2021) - [i47]Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou, Nathan Kallus, Mark J. van der Laan:
Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning. CoRR abs/2106.01723 (2021) - [i46]Nikos Vlassis, Ashok Chandrashekar, Fernando Amat Gil, Nathan Kallus:
Control Variates for Slate Off-Policy Evaluation. CoRR abs/2106.07914 (2021) - [i45]James McInerney, Nathan Kallus:
Residual Overfit Method of Exploration. CoRR abs/2110.02919 (2021) - [i44]Nathan Kallus, Angela Zhou:
Stateful Offline Contextual Policy Evaluation and Learning. CoRR abs/2110.10081 (2021) - [i43]Andrew Bennett, Nathan Kallus:
Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes. CoRR abs/2110.15332 (2021) - [i42]Angela Zhou, Andrew Koo, Nathan Kallus, Rene Ropac, Richard Peterson, Stephen Koppel, Tiffany Bergin:
An Empirical Evaluation of the Impact of New York's Bail Reform on Crime Using Synthetic Controls. CoRR abs/2111.08664 (2021) - [i41]Jacob Dorn, Kevin Guo, Nathan Kallus:
Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding. CoRR abs/2112.11449 (2021) - 2020
- [j7]Nathan Kallus, Madeleine Udell:
Dynamic Assortment Personalization in High Dimensions. Oper. Res. 68(4): 1020-1037 (2020) - [j6]Nathan Kallus:
Generalized Optimal Matching Methods for Causal Inference. J. Mach. Learn. Res. 21: 62:1-62:54 (2020) - [j5]Nathan Kallus, Masatoshi Uehara:
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes. J. Mach. Learn. Res. 21: 167:1-167:63 (2020) - [j4]Dimitris Bertsimas, Nathan Kallus:
From Predictive to Prescriptive Analytics. Manag. Sci. 66(3): 1025-1044 (2020) - [c29]Yichun Hu, Nathan Kallus, Xiaojie Mao:
Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes. COLT 2020: 2007-2010 - [c28]Nathan Kallus, Xiaojie Mao, Angela Zhou:
Assessing algorithmic fairness with unobserved protected class using data combination. FAT* 2020: 110 - [c27]Andrew Bennett, Nathan Kallus:
Efficient Policy Learning from Surrogate-Loss Classification Reductions. ICML 2020: 788-798 - [c26]Nathan Kallus:
DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training. ICML 2020: 5067-5077 - [c25]Nathan Kallus, Masatoshi Uehara:
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation. ICML 2020: 5078-5088 - [c24]Nathan Kallus, Masatoshi Uehara:
Statistically Efficient Off-Policy Policy Gradients. ICML 2020: 5089-5100 - [c23]Nathan Kallus, Masatoshi Uehara:
Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies. NeurIPS 2020 - [c22]Nathan Kallus, Angela Zhou:
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning. NeurIPS 2020 - [i40]Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David A. Sontag:
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects. CoRR abs/2001.07426 (2020) - [i39]Nathan Kallus, Masatoshi Uehara:
Statistically Efficient Off-Policy Policy Gradients. CoRR abs/2002.04014 (2020) - [i38]Nathan Kallus, Angela Zhou:
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning. CoRR abs/2002.04518 (2020) - [i37]Andrew Bennett, Nathan Kallus:
Efficient Policy Learning from Surrogate-Loss Classification Reductions. CoRR abs/2002.05153 (2020) - [i36]Nathan Kallus, Xiaojie Mao:
On the role of surrogates in the efficient estimation of treatment effects with limited outcome data. CoRR abs/2003.12408 (2020) - [i35]Nathan Kallus:
Comment: Entropy Learning for Dynamic Treatment Regimes. CoRR abs/2004.02778 (2020) - [i34]Yichun Hu, Nathan Kallus:
DTR Bandit: Learning to Make Response-Adaptive Decisions With Low Regret. CoRR abs/2005.02791 (2020) - [i33]Nathan Kallus, Masatoshi Uehara:
Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning. CoRR abs/2006.03886 (2020) - [i32]Nathan Kallus, Masatoshi Uehara:
Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies. CoRR abs/2006.03900 (2020) - [i31]Andrew Bennett, Nathan Kallus, Lihong Li, Ali Mousavi:
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders. CoRR abs/2007.13893 (2020) - [i30]Nathan Kallus, Xiaojie Mao:
Stochastic Optimization Forests. CoRR abs/2008.07473 (2020) - [i29]Nathan Kallus, Yuta Saito, Masatoshi Uehara:
Optimal Off-Policy Evaluation from Multiple Logging Policies. CoRR abs/2010.11002 (2020) - [i28]Yichun Hu, Nathan Kallus, Xiaojie Mao:
Fast Rates for Contextual Linear Optimization. CoRR abs/2011.03030 (2020) - [i27]Nathan Kallus:
Rejoinder: New Objectives for Policy Learning. CoRR abs/2012.03130 (2020) - [i26]Andrew Bennett, Nathan Kallus:
The Variational Method of Moments. CoRR abs/2012.09422 (2020) - [i25]Nathan Kallus, Angela Zhou:
Fairness, Welfare, and Equity in Personalized Pricing. CoRR abs/2012.11066 (2020)
2010 – 2019
- 2019
- [c21]Nathan Kallus, Xiaojie Mao, Angela Zhou:
Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding. AISTATS 2019: 2281-2290 - [c20]Jiahao Chen, Nathan Kallus, Xiaojie Mao, Geoffry Svacha, Madeleine Udell:
Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved. FAT 2019: 339-348 - [c19]Nathan Kallus:
Classifying Treatment Responders Under Causal Effect Monotonicity. ICML 2019: 3201-3210 - [c18]Nathan Kallus, Masatoshi Uehara:
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning. NeurIPS 2019: 3320-3329 - [c17]Nathan Kallus, Angela Zhou:
Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds. NeurIPS 2019: 3421-3432 - [c16]Nathan Kallus, Angela Zhou:
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric. NeurIPS 2019: 3433-3443 - [c15]Andrew Bennett, Nathan Kallus, Tobias Schnabel:
Deep Generalized Method of Moments for Instrumental Variable Analysis. NeurIPS 2019: 3559-3569 - [c14]Andrew Bennett, Nathan Kallus:
Policy Evaluation with Latent Confounders via Optimal Balance. NeurIPS 2019: 4827-4837 - [i24]Nathan Kallus:
Classifying Treatment Responders Under Causal Effect Monotonicity. CoRR abs/1902.05482 (2019) - [i23]Nathan Kallus, Angela Zhou:
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric. CoRR abs/1902.05826 (2019) - [i22]Andrew Bennett, Nathan Kallus, Tobias Schnabel:
Deep Generalized Method of Moments for Instrumental Variable Analysis. CoRR abs/1905.12495 (2019) - [i21]Vishal Gupta, Nathan Kallus:
Data-Pooling in Stochastic Optimization. CoRR abs/1906.00255 (2019) - [i20]Nathan Kallus, Xiaojie Mao, Angela Zhou:
Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination. CoRR abs/1906.00285 (2019) - [i19]Nathan Kallus, Angela Zhou:
Assessing Disparate Impacts of Personalized Interventions: Identifiability and Bounds. CoRR abs/1906.01552 (2019) - [i18]Nathan Kallus, Masatoshi Uehara:
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning. CoRR abs/1906.03735 (2019) - [i17]Nathan Kallus:
More Efficient Policy Learning via Optimal Retargeting. CoRR abs/1906.08611 (2019) - [i16]Andrew Bennett, Nathan Kallus:
Policy Evaluation with Latent Confounders via Optimal Balance. CoRR abs/1908.01920 (2019) - [i15]Nathan Kallus, Masatoshi Uehara:
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes. CoRR abs/1908.08526 (2019) - [i14]Yichun Hu, Nathan Kallus, Xiaojie Mao:
Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes. CoRR abs/1909.02553 (2019) - [i13]Nathan Kallus, Masatoshi Uehara:
Efficiently Breaking the Curse of Horizon: Double Reinforcement Learning in Infinite-Horizon Processes. CoRR abs/1909.05850 (2019) - [i12]Nathan Kallus, Xiaojie Mao, Masatoshi Uehara:
Localized Debiased Machine Learning: Efficient Estimation of Quantile Treatment Effects, Conditional Value at Risk, and Beyond. CoRR abs/1912.12945 (2019) - 2018
- [j3]Dimitris Bertsimas, Vishal Gupta, Nathan Kallus:
Data-driven robust optimization. Math. Program. 167(2): 235-292 (2018) - [j2]Dimitris Bertsimas, Vishal Gupta, Nathan Kallus:
Robust sample average approximation. Math. Program. 171(1-2): 217-282 (2018) - [c13]Nathan Kallus, Angela Zhou:
Policy Evaluation and Optimization with Continuous Treatments. AISTATS 2018: 1243-1251 - [c12]Nathan Kallus:
Instrument-Armed Bandits. ALT 2018: 529-546 - [c11]Nathan Kallus, Angela Zhou:
Residual Unfairness in Fair Machine Learning from Prejudiced Data. ICML 2018: 2444-2453 - [c10]Nathan Kallus, Xiaojie Mao, Madeleine Udell:
Causal Inference with Noisy and Missing Covariates via Matrix Factorization. NeurIPS 2018: 6921-6932 - [c9]Nathan Kallus:
Balanced Policy Evaluation and Learning. NeurIPS 2018: 8909-8920 - [c8]Nathan Kallus, Angela Zhou:
Confounding-Robust Policy Improvement. NeurIPS 2018: 9289-9299 - [c7]Nathan Kallus, Aahlad Manas Puli, Uri Shalit:
Removing Hidden Confounding by Experimental Grounding. NeurIPS 2018: 10911-10920 - [i11]Nathan Kallus, Angela Zhou:
Policy Evaluation and Optimization with Continuous Treatments. CoRR abs/1802.06037 (2018) - [i10]Nathan Kallus, Angela Zhou:
Confounding-Robust Policy Improvement. CoRR abs/1805.08593 (2018) - [i9]Nathan Kallus, Xiaojie Mao, Madeleine Udell:
Causal Inference with Noisy and Missing Covariates via Matrix Factorization. CoRR abs/1806.00811 (2018) - [i8]Nathan Kallus, Angela Zhou:
Residual Unfairness in Fair Machine Learning from Prejudiced Data. CoRR abs/1806.02887 (2018) - [i7]