Nathan Kallus

ORCID

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 160

Top Venues

Publications

Kaiwen Wang, Dawen Liang, Nathan Kallus, Wen Sun
Risk-Sensitive RL with Optimized Certainty Equivalents via Reduction to Standard RL. CoRR (2024)
Aurélien Bibaut, Winston Chou, Simon Ejdemyr, Nathan Kallus
Learning the Covariance of Treatment Effects Across Many Weak Experiments. KDD (2024)
Su Jia, Peter I. Frazier, Nathan Kallus
Multi-Armed Bandits with Interference. CoRR (2024)
Miruna Oprescu, Andrew Bennett, Nathan Kallus
Low-rank MDPs with Continuous Action Spaces. AISTATS (2024)
Zhankui He, Zhouhang Xie, Harald Steck, Dawen Liang, Rahul Jha, Nathan Kallus, Julian J. McAuley
Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation. CoRR (2024)
Harald Steck, Chaitanya Ekanadham, Nathan Kallus
Is Cosine-Similarity of Embeddings Really About Similarity? WWW (Companion Volume) (2024)
Kaiwen Wang, Owen Oertell, Alekh Agarwal, Nathan Kallus, Wen Sun
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning. CoRR (2024)
Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, Vasilis Syrgkanis
Applied Causal Inference Powered by ML and AI. CoRR (2024)
Noveen Sachdeva, Lequn Wang, Dawen Liang, Nathan Kallus, Julian J. McAuley
Off-Policy Evaluation for Large Action Spaces via Policy Convolution. WWW (2024)
Miruna Oprescu, Nathan Kallus
Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data. CoRR (2024)
James McInerney, Nathan Kallus
Hessian-Free Laplace in Bayesian Deep Learning. CoRR (2024)
Yichun Hu, Nathan Kallus, Xiaojie Mao, Yanchen Wu
Contextual Linear Optimization with Bandit Feedback. CoRR (2024)
Alex Ayoub, Kaiwen Wang, Vincent Liu, Samuel Robertson, James McInerney, Dawen Liang, Nathan Kallus, Csaba Szepesvári
Switching the Loss Reduces the Cost in Batch Reinforcement Learning. CoRR (2024)
Andrew Bennett, Nathan Kallus
Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes. Oper. Res. 72 (3) (2024)
Andrew Bennett, Nathan Kallus, Miruna Oprescu, Wen Sun, Kaiwen Wang
Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes. CoRR (2024)
Harald Steck, Chaitanya Ekanadham, Nathan Kallus
Is Cosine-Similarity of Embeddings Really About Similarity? CoRR (2024)
Brian Cho, Kyra Gan, Nathan Kallus
Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams. CoRR (2024)
Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun
Provable Offline Preference-Based Reinforcement Learning. ICLR (2024)
Kaiwen Wang, Nathan Kallus, Wen Sun
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR. CoRR (2023)
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 (2023)
Kaiwen Wang, Kevin Zhou, Runzhe Wu, Nathan Kallus, Wen Sun
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning. CoRR (2023)
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 (2023)
Andrew Bennett, Dipendra Misra, Nathan Kallus
Provable Safe Reinforcement Learning with Binary Feedback. AISTATS (2023)
Andrew Bennett, Nathan Kallus, Miruna Oprescu
Low-Rank MDPs with Continuous Action Spaces. CoRR (2023)
Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun
Refined Value-Based Offline RL under Realizability and Partial Coverage. CoRR (2023)
Su Jia, Qian Xie, Nathan Kallus, Peter I. Frazier
Smooth Non-Stationary Bandits. CoRR (2023)
Kaiwen Wang, Kevin Zhou, Runzhe Wu, Nathan Kallus, Wen Sun
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning. NeurIPS (2023)
Su Jia, Qian Xie, Nathan Kallus, Peter I. Frazier
Smooth Non-stationary Bandits. ICML (2023)
Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun
Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage. NeurIPS (2023)
Nathan Kallus, Xiaojie Mao
Stochastic Optimization Forests. Manag. Sci. 69 (4) (2023)
Kaiwen Wang, Nathan Kallus, Wen Sun
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR. ICML (2023)
Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun
Provable Offline Reinforcement Learning with Human Feedback. CoRR (2023)
Su Jia, Sohom Bhattacharya, Nathan Kallus, Christina Lee Yu
Faster Rates for Switchback Experiments. CoRR (2023)
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)
Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara
Convergence Guarantees without Identification or Closedness. COLT (2023)
Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara
Inference on Strongly Identified Functionals of Weakly Identified Functions. COLT (2023)
Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara
Source Condition Double Robust Inference on Functionals of Inverse Problems. CoRR (2023)
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. CIKM (2023)
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 (2023)
Dimitris Bertsimas, Nathan Kallus
The Power and Limits of Predictive Approaches to Observational Data-Driven Optimization: The Case of Pricing. INFORMS J. Optim. 5 (1) (2023)
Noveen Sachdeva, Lequn Wang, Dawen Liang, Nathan Kallus, Julian J. McAuley
Off-Policy Evaluation for Large Action Spaces via Policy Convolution. CoRR (2023)
Nathan Kallus
Treatment Effect Risk: Bounds and Inference. Manag. Sci. 69 (8) (2023)
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. NeurIPS (2023)
Kaiwen Wang, Junxiong Wang, Yueying Li, Nathan Kallus, Immanuel Trummer, Wen Sun
JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning. CoRR (2023)
Nathan Kallus, Miruna Oprescu
Robust and Agnostic Learning of Conditional Distributional Treatment Effects. AISTATS (2023)
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)
Nathan Kallus
Treatment Effect Risk: Bounds and Inference. FAccT (2022)
Jonathan D. Chang, Kaiwen Wang, Nathan Kallus, Wen Sun
Learning Bellman Complete Representations for Offline Policy Evaluation. CoRR (2022)
Shervin Ardeshir, Cristina Segalin, Nathan Kallus
Estimating Structural Disparities for Face Models. CoRR (2022)
Vishal Gupta, Nathan Kallus
Data Pooling in Stochastic Optimization. Manag. Sci. 68 (3) (2022)
Nathan Kallus, Xiaojie Mao, Kaiwen Wang, Zhengyuan Zhou
Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning. CoRR (2022)
Nathan Kallus, Masatoshi Uehara
Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning. Oper. Res. 70 (6) (2022)
Nathan Kallus
What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment. NeurIPS (2022)
Nathan Kallus, Angela Zhou
Stateful Offline Contextual Policy Evaluation and Learning. AISTATS (2022)
Nathan Kallus, Xiaojie Mao, Angela Zhou
Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination. Manag. Sci. 68 (3) (2022)
Nathan Kallus, James McInerney
The Implicit Delta Method. NeurIPS (2022)
Shervin Ardeshir, Cristina Segalin, Nathan Kallus
Estimating Structural Disparities for Face Models. CVPR (2022)
Jonathan Chang, Kaiwen Wang, Nathan Kallus, Wen Sun
Learning Bellman Complete Representations for Offline Policy Evaluation. ICML (2022)
Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. CoRR (2022)
Andrew Bennett, Dipendra Misra, Nathan Kallus
Provable Safe Reinforcement Learning with Binary Feedback. CoRR (2022)
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 (2022)
Yichun Hu, Nathan Kallus, Xiaojie Mao
Smooth Contextual Bandits: Bridging the Parametric and Nondifferentiable Regret Regimes. Oper. Res. 70 (6) (2022)
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 (2022)
Nathan Kallus, Miruna Oprescu
Robust and Agnostic Learning of Conditional Distributional Treatment Effects. CoRR (2022)
Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. NeurIPS (2022)
Nathan Kallus, Xiaojie Mao, Kaiwen Wang, Zhengyuan Zhou
Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning. ICML (2022)
Yichun Hu, Nathan Kallus, Xiaojie Mao
Fast Rates for Contextual Linear Optimization. Manag. Sci. 68 (6) (2022)
Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. CoRR (2022)
Nathan Kallus
What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment. CoRR (2022)
Nathan Kallus, James McInerney
The Implicit Delta Method. CoRR (2022)
Masatoshi Uehara, Chengchun Shi, Nathan Kallus
A Review of Off-Policy Evaluation in Reinforcement Learning. CoRR (2022)
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 (2021)
Nathan Kallus, Xiaojie Mao, Masatoshi Uehara
Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach. CoRR (2021)
Nikos Vlassis, Ashok Chandrashekar, Fernando Amat Gil, Nathan Kallus
Control Variates for Slate Off-Policy Evaluation. CoRR (2021)
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 (2021)
Yichun Hu, Nathan Kallus, Masatoshi Uehara
Fast Rates for the Regret of Offline Reinforcement Learning. CoRR (2021)
Yichun Hu, Nathan Kallus, Masatoshi Uehara
Fast Rates for the Regret of Offline Reinforcement Learning. COLT (2021)
Andrew Bennett, Nathan Kallus
Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes. CoRR (2021)
Nathan Kallus, Angela Zhou
Stateful Offline Contextual Policy Evaluation and Learning. CoRR (2021)
Andrew Bennett, Nathan Kallus, Lihong Li, Ali Mousavi
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders. AISTATS (2021)
Nikos Vlassis, Ashok Chandrashekar, Fernando Amat Gil, Nathan Kallus
Control Variates for Slate Off-Policy Evaluation. NeurIPS (2021)
Nathan Kallus, Angela Zhou
Minimax-Optimal Policy Learning Under Unobserved Confounding. Manag. Sci. 67 (5) (2021)
Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou, Nathan Kallus, Mark J. van der Laan
Post-Contextual-Bandit Inference. CoRR (2021)
Aurélien Bibaut, Maria Dimakopoulou, Nathan Kallus, Antoine Chambaz, Mark J. van der Laan
Post-Contextual-Bandit Inference. NeurIPS (2021)
Nathan Kallus, Angela Zhou
Fairness, Welfare, and Equity in Personalized Pricing. FAccT (2021)
Jacob Dorn, Kevin Guo, Nathan Kallus
Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding. CoRR (2021)
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 (2021)
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)
James McInerney, Nathan Kallus
Residual Overfit Method of Exploration. CoRR (2021)
Nathan Kallus, Yuta Saito, Masatoshi Uehara
Optimal Off-Policy Evaluation from Multiple Logging Policies. ICML (2021)
Nathan Kallus, Xiaojie Mao, Angela Zhou
Assessing algorithmic fairness with unobserved protected class using data combination. FAT* (2020)
Andrew Bennett, Nathan Kallus
The Variational Method of Moments. CoRR (2020)
Nathan Kallus, Masatoshi Uehara
Statistically Efficient Off-Policy Policy Gradients. ICML (2020)
Nathan Kallus, Madeleine Udell
Dynamic Assortment Personalization in High Dimensions. Oper. Res. 68 (4) (2020)
Nathan Kallus, Masatoshi Uehara
Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies. NeurIPS (2020)
Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David A. Sontag
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects. CoRR (2020)
Andrew Bennett, Nathan Kallus
Efficient Policy Learning from Surrogate-Loss Classification Reductions. CoRR (2020)
Yichun Hu, Nathan Kallus
DTR Bandit: Learning to Make Response-Adaptive Decisions With Low Regret. CoRR (2020)
Nathan Kallus
Rejoinder: New Objectives for Policy Learning. CoRR (2020)
Nathan Kallus, Masatoshi Uehara
Statistically Efficient Off-Policy Policy Gradients. CoRR (2020)

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Publication Activity (10 Years)

Top Topics

Top Venues

Nathan Kallus