Nathan Kallus - RankMe

220 papers A* 55A 14B 1Journal 139Unranked 11

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Causal Inference on Networks under Misspecified Exposure Mappings: A Partial Identification Framework. CoRR Maresa Schröder, Miruna Oprescu, Stefan Feuerriegel, Nathan Kallus
2026	J	`jnl`	Efficient Inference after Directionally Stable Adaptive Experiments. CoRR Zikai Shen, Houssam Zenati, Nathan Kallus, Arthur Gretton, Koulik Khamaru, Aurélien Bibaut
2026	A*	`conf`	Exploration Sizing via Model-Predictive Control. WWW Ethan Che, Hakan Ceylan, James McInerney, Nathan Kallus
2026	J	`jnl`	Exploration in the Limit. CoRR Brian M. Cho, Nathan Kallus
2026	J	`jnl`	Functional Natural Policy Gradients. CoRR Aurélien F. Bibaut, Houssam Zenati, Thibaud Rahier, Nathan Kallus
2026	J	`jnl`	GAAVI: Global Asymptotic Anytime Valid Inference for the Conditional Mean Function. CoRR Brian M. Cho, Raaz Dwivedi, Nathan Kallus
2025	A*	`conf`	A Reductions Approach to Risk-Sensitive Reinforcement Learning with Optimized Certainty Equivalents. ICML Kaiwen Wang, Dawen Liang, Nathan Kallus, Wen Sun
2025	A	`conf`	Anytime-Valid A/B Testing of Counting Processes. AISTATS Michael Lindon, Nathan Kallus
2025	J	`jnl`	Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference. CoRR Lars van der Laan, David Hubbard, Allen Tran, Nathan Kallus, Aurélien Bibaut
2025	J	`jnl`	Bellman Calibration for V-Learning in Offline Reinforcement Learning. CoRR Lars van der Laan, Nathan Kallus
2025	—	`conf`	CSPI-MT: Calibrated Safe Policy Improvement with Multiple Testing for Threshold Policies. KDD (1) Brian M. Cho, Ana-Roxana Pop, Kyra Gan, Sam Corbett-Davies, Israel Nir, Ariel Evnine, Nathan Kallus
2025	A*	`conf`	Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems. WWW Yaochen Zhu, Chao Wan, Harald Steck, Dawen Liang, Yesu Feng, Nathan Kallus, Jundong Li
2025	J	`jnl`	Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems. CoRR Yaochen Zhu, Chao Wan, Harald Steck, Dawen Liang, Yesu Feng, Nathan Kallus, Jundong Li
2025	J	`jnl`	DiFFPO: Training Diffusion LLMs to Reason Fast and Furious via Reinforcement Learning. CoRR Hanyang Zhao, Dawen Liang, Wenpin Tang, David D. Yao, Nathan Kallus
2025	A*	`conf`	Does Weighting Improve Matrix Factorization for Recommender Systems? WWW Alex Ayoub, Samuel Robertson, Dawen Liang, Harald Steck, Nathan Kallus
2025	J	`jnl`	Does Weighting Improve Matrix Factorization for Recommender Systems? CoRR Alex Ayoub, Samuel Robertson, Dawen Liang, Harald Steck, Nathan Kallus
2025	J	`jnl`	Efficient Adaptive Experimentation with Non-Compliance. CoRR Miruna Oprescu, Brian M. Cho, Nathan Kallus
2025	J	`jnl`	Efficient Inference for Inverse Reinforcement Learning and Dynamic Discrete Choice Models. CoRR Lars van der Laan, Aurélien Bibaut, Nathan Kallus
2025	J	`jnl`	Entropy After <\Think> for reasoning model early exiting. CoRR Xi Wang, James McInerney, Lequn Wang, Nathan Kallus
2025	—	`conf`	Evaluating Decision Rules Across Many Weak Experiments. KDD (2) Winston Chou, Colin Gray, Nathan Kallus, Aurélien Bibaut, Simon Ejdemyr
2025	J	`jnl`	Fast Rates for the Regret of Offline Reinforcement Learning. Math. Oper. Res. Yichun Hu, Nathan Kallus, Masatoshi Uehara
2025	J	`jnl`	Fitted Q Evaluation Without Bellman Completeness via Stationary Weighting. CoRR Lars van der Laan, Nathan Kallus
2025	J	`jnl`	From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System. CoRR Rohan Surana, Junda Wu, Zhouhang Xie, Yu Xia, Harald Steck, Dawen Liang, Nathan Kallus, Julian J. McAuley
2025	J	`jnl`	GST-UNet: Spatiotemporal Causal Inference with Time-Varying Confounders. CoRR Miruna Oprescu, David Keetae Park, Xihaier Luo, Shinjae Yoo, Nathan Kallus
2025	J	`jnl`	Inverse Reinforcement Learning Using Just Classification and a Few Regressions. CoRR Lars van der Laan, Nathan Kallus, Aurélien Bibaut
2025	—	`conf`	LLM-based Conversational Recommendation Agents with Collaborative Verbalized Experience. EMNLP (Findings) Yaochen Zhu, Harald Steck, Dawen Liang, Yinhan He, Nathan Kallus, Jundong Li
2025	A*	`conf`	Multi-Armed Bandits with Interference: Bridging Causal Inference and Adversarial Bandits. ICML Su Jia, Peter I. Frazier, Nathan Kallus
2025	J	`jnl`	Optimization of Epsilon-Greedy Exploration. CoRR Ethan Che, Hakan Ceylan, James McInerney, Nathan Kallus
2025	J	`jnl`	Q♯: Provably Optimal Distributional RL for LLM Post-Training. CoRR Jin Peng Zhou, Kaiwen Wang, Jonathan D. Chang, Zhaolin Gao, Nathan Kallus, Kilian Q. Weinberger, Kianté Brantley, Wen Sun
2025	J	`jnl`	Rank-GRPO: Training LLM-based Conversational Recommender Systems with Reinforcement Learning. CoRR Yaochen Zhu, Harald Steck, Dawen Liang, Yinhan He, Vito Ostuni, Jundong Li, Nathan Kallus
2025	A	`conf`	Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation. WSDM Zhankui He, Zhouhang Xie, Harald Steck, Dawen Liang, Rahul Jha, Nathan Kallus, Julian J. McAuley
2025	A	`conf`	Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed Bandits. AISTATS Brian M. Cho, Dominik Meier, Kyra Gan, Nathan Kallus
2025	J	`jnl`	SNPL: Simultaneous Policy Learning and Evaluation for Safe Multi-Objective Policy Improvement. CoRR Brian M. Cho, Ana-Roxana Pop, Ariel Evnine, Nathan Kallus
2025	J	`jnl`	Semiparametric Preference Optimization: Your Language Model is Secretly a Single-Index Model. CoRR Nathan Kallus
2025	J	`jnl`	Simulation-Based Inference for Adaptive Experiments. CoRR Brian M. Cho, Aurélien Bibaut, Nathan Kallus
2025	J	`jnl`	Stationary Reweighting Yields Local Convergence of Soft Fitted Q-Iteration. CoRR Lars van der Laan, Nathan Kallus
2025	J	`jnl`	The Value of Personalized Recommendations: Evidence from Netflix. CoRR Kevin Zielnicki, Guy Aridor, Aurélien Bibaut, Allen Tran, Winston Chou, Nathan Kallus
2025	J	`jnl`	Value-Guided Search for Efficient Chain-of-Thought Reasoning. CoRR Kaiwen Wang, Jin Peng Zhou, Jonathan D. Chang, Zhaolin Gao, Nathan Kallus, Kianté Brantley, Wen Sun
2025	A	`conf`	Variation Due to Regularization Tractably Recovers Bayesian Deep Learning Uncertainty. AISTATS James McInerney, Nathan Kallus
2024	J	`jnl`	Adjusting Regression Models for Conditional Uncertainty Calibration. CoRR Ruijiang Gao, Mingzhang Yin, James McInerney, Nathan Kallus
2024	J	`jnl`	Adjusting regression models for conditional uncertainty calibration. Mach. Learn. Ruijiang Gao, Mingzhang Yin, James McInerney, Nathan Kallus
2024	J	`jnl`	Applied Causal Inference Powered by ML and AI. CoRR Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, Vasilis Syrgkanis
2024	J	`jnl`	CSPI-MT: Calibrated Safe Policy Improvement with Multiple Testing for Threshold Policies. CoRR Brian M. Cho, Ana-Roxana Pop, Kyra Gan, Sam Corbett-Davies, Israel Nir, Ariel Evnine, Nathan Kallus
2024	A*	`conf`	Contextual Linear Optimization with Bandit Feedback. NeurIPS Yichun Hu, Nathan Kallus, Xiaojie Mao, Yanchen Wu
2024	J	`jnl`	Contextual Linear Optimization with Bandit Feedback. CoRR Yichun Hu, Nathan Kallus, Xiaojie Mao, Yanchen Wu
2024	A*	`conf`	Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes. NeurIPS Andrew Bennett, Nathan Kallus, Miruna Oprescu, Wen Sun, Kaiwen Wang
2024	J	`jnl`	Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes. CoRR Andrew Bennett, Nathan Kallus, Miruna Oprescu, Wen Sun, Kaiwen Wang
2024	A*	`conf`	Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data. NeurIPS Miruna Oprescu, Nathan Kallus
2024	J	`jnl`	Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data. CoRR Miruna Oprescu, Nathan Kallus
2024	J	`jnl`	Hessian-Free Laplace in Bayesian Deep Learning. CoRR James McInerney, Nathan Kallus
2024	A*	`conf`	Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments. ICML Allen Tran, Aurélien Bibaut, Nathan Kallus
2024	—	`conf`	Is Cosine-Similarity of Embeddings Really About Similarity? WWW (Companion Volume) Harald Steck, Chaitanya Ekanadham, Nathan Kallus
2024	J	`jnl`	Is Cosine-Similarity of Embeddings Really About Similarity? CoRR Harald Steck, Chaitanya Ekanadham, Nathan Kallus
2024	J	`jnl`	JoinGym: An Efficient Join Order Selection Environment. RLJ Junxiong Wang, Kaiwen Wang, Yueying Li, Nathan Kallus, Immanuel Trummer, Wen Sun
2024	A*	`conf`	Learning the Covariance of Treatment Effects Across Many Weak Experiments. KDD Aurélien Bibaut, Winston Chou, Simon Ejdemyr, Nathan Kallus
2024	J	`jnl`	Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond. J. Mach. Learn. Res. Nathan Kallus, Xiaojie Mao, Masatoshi Uehara
2024	A	`conf`	Low-rank MDPs with Continuous Action Spaces. AISTATS Miruna Oprescu, Andrew Bennett, Nathan Kallus
2024	A*	`conf`	More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning. ICML Kaiwen Wang, Owen Oertell, Alekh Agarwal, Nathan Kallus, Wen Sun
2024	J	`jnl`	More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning. CoRR Kaiwen Wang, Owen Oertell, Alekh Agarwal, Nathan Kallus, Wen Sun
2024	J	`jnl`	Multi-Armed Bandits with Interference. CoRR Su Jia, Peter I. Frazier, Nathan Kallus
2024	A	`conf`	Neighborhood-Based Collaborative Filtering for Conversational Recommendation. RecSys Zhouhang Xie, Junda Wu, Hyunsik Jeon, Zhankui He, Harald Steck, Rahul Jha, Dawen Liang, Nathan Kallus, Julian J. McAuley
2024	A*	`conf`	Off-Policy Evaluation for Large Action Spaces via Policy Convolution. WWW Noveen Sachdeva, Lequn Wang, Dawen Liang, Nathan Kallus, Julian J. McAuley
2024	A*	`conf`	Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams. ICML Brian Cho, Kyra Gan, Nathan Kallus
2024	J	`jnl`	Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams. CoRR Brian Cho, Kyra Gan, Nathan Kallus
2024	A*	`conf`	Provable Offline Preference-Based Reinforcement Learning. ICLR Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun
2024	J	`jnl`	Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes. Oper. Res. Andrew Bennett, Nathan Kallus
2024	J	`jnl`	Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation. CoRR Zhankui He, Zhouhang Xie, Harald Steck, Dawen Liang, Rahul Jha, Nathan Kallus, Julian J. McAuley
2024	J	`jnl`	Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed Bandits. CoRR Brian Cho, Dominik Meier, Kyra Gan, Nathan Kallus
2024	J	`jnl`	Risk-Sensitive RL with Optimized Certainty Equivalents via Reduction to Standard RL. CoRR Kaiwen Wang, Dawen Liang, Nathan Kallus, Wen Sun
2024	A*	`conf`	Switching the Loss Reduces the Cost in Batch Reinforcement Learning. ICML Alex Ayoub, Kaiwen Wang, Vincent Liu, Samuel Robertson, James McInerney, Dawen Liang, Nathan Kallus, Csaba Szepesvári
2024	J	`jnl`	Switching the Loss Reduces the Cost in Batch Reinforcement Learning. CoRR Alex Ayoub, Kaiwen Wang, Vincent Liu, Samuel Robertson, James McInerney, Dawen Liang, Nathan Kallus, Csaba Szepesvári
2024	J	`jnl`	The Central Role of the Loss Function in Reinforcement Learning. CoRR Kaiwen Wang, Nathan Kallus, Wen Sun
2023	A*	`conf`	B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding. ICML Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit
2023	J	`jnl`	B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding. CoRR Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit
2023	A*	`conf`	Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. ICML Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
2023	J	`jnl`	Faster Rates for Switchback Experiments. CoRR Su Jia, Sohom Bhattacharya, Nathan Kallus, Christina Lee Yu
2023	A*	`conf`	Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. NeurIPS Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang, Nathan Kallus, Chengchun Shi, Wen Sun
2023	A*	`conf`	Inference on Strongly Identified Functionals of Weakly Identified Functions. COLT Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara
2023	J	`jnl`	JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning. CoRR Kaiwen Wang, Junxiong Wang, Yueying Li, Nathan Kallus, Immanuel Trummer, Wen Sun
2023	A	`conf`	Large Language Models as Zero-Shot Conversational Recommenders. CIKM Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, Julian J. McAuley
2023	J	`jnl`	Large Language Models as Zero-Shot Conversational Recommenders. CoRR Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, Julian J. McAuley
2023	J	`jnl`	Low-Rank MDPs with Continuous Action Spaces. CoRR Andrew Bennett, Nathan Kallus, Miruna Oprescu
2023	J	`jnl`	Minimax Instrumental Variable Regression and $L_2$ Convergence Guarantees without Identification or Closedness. CoRR Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara
2023	A*	`conf`	Minimax Instrumental Variable Regression and L COLT Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara
2023	A*	`conf`	Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR. ICML Kaiwen Wang, Nathan Kallus, Wen Sun
2023	J	`jnl`	Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR. CoRR Kaiwen Wang, Nathan Kallus, Wen Sun
2023	J	`jnl`	Off-Policy Evaluation for Large Action Spaces via Policy Convolution. CoRR Noveen Sachdeva, Lequn Wang, Dawen Liang, Nathan Kallus, Julian J. McAuley
2023	A*	`conf`	Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage. NeurIPS Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun
2023	J	`jnl`	Provable Offline Reinforcement Learning with Human Feedback. CoRR Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun
2023	A	`conf`	Provable Safe Reinforcement Learning with Binary Feedback. AISTATS Andrew Bennett, Dipendra Misra, Nathan Kallus
2023	J	`jnl`	Refined Value-Based Offline RL under Realizability and Partial Coverage. CoRR Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun
2023	A	`conf`	Robust and Agnostic Learning of Conditional Distributional Treatment Effects. AISTATS Nathan Kallus, Miruna Oprescu
2023	J	`jnl`	Smooth Non-Stationary Bandits. CoRR Su Jia, Qian Xie, Nathan Kallus, Peter I. Frazier
2023	A*	`conf`	Smooth Non-stationary Bandits. ICML Su Jia, Qian Xie, Nathan Kallus, Peter I. Frazier
2023	J	`jnl`	Source Condition Double Robust Inference on Functionals of Inverse Problems. CoRR Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara
2023	J	`jnl`	Stochastic Optimization Forests. Manag. Sci. Nathan Kallus, Xiaojie Mao
2023	A*	`conf`	The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning. NeurIPS Kaiwen Wang, Kevin Zhou, Runzhe Wu, Nathan Kallus, Wen Sun
2023	J	`jnl`	The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning. CoRR Kaiwen Wang, Kevin Zhou, Runzhe Wu, Nathan Kallus, Wen Sun
2023	J	`jnl`	The Power and Limits of Predictive Approaches to Observational Data-Driven Optimization: The Case of Pricing. INFORMS J. Optim. Dimitris Bertsimas, Nathan Kallus
2023	J	`jnl`	Treatment Effect Risk: Bounds and Inference. Manag. Sci. Nathan Kallus
2022	J	`jnl`	A Review of Off-Policy Evaluation in Reinforcement Learning. CoRR Masatoshi Uehara, Chengchun Shi, Nathan Kallus
2022	J	`jnl`	Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination. Manag. Sci. Nathan Kallus, Xiaojie Mao, Angela Zhou
2022	J	`jnl`	Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. CoRR Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
2022	J	`jnl`	Data Pooling in Stochastic Optimization. Manag. Sci. Vishal Gupta, Nathan Kallus
2022	A*	`conf`	Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning. ICML Nathan Kallus, Xiaojie Mao, Kaiwen Wang, Zhengyuan Zhou
2022	J	`jnl`	Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning. CoRR Nathan Kallus, Xiaojie Mao, Kaiwen Wang, Zhengyuan Zhou
2022	J	`jnl`	Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning. Oper. Res. Nathan Kallus, Masatoshi Uehara
2022	A*	`conf`	Estimating Structural Disparities for Face Models. CVPR Shervin Ardeshir, Cristina Segalin, Nathan Kallus
2022	J	`jnl`	Estimating Structural Disparities for Face Models. CoRR Shervin Ardeshir, Cristina Segalin, Nathan Kallus
2022	J	`jnl`	Fast Rates for Contextual Linear Optimization. Manag. Sci. Yichun Hu, Nathan Kallus, Xiaojie Mao
2022	J	`jnl`	Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. CoRR Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang, Nathan Kallus, Chengchun Shi, Wen Sun
2022	J	`jnl`	Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects. J. Mach. Learn. Res. Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David A. Sontag
2022	A*	`conf`	Learning Bellman Complete Representations for Offline Policy Evaluation. ICML Jonathan D. Chang, Kaiwen Wang, Nathan Kallus, Wen Sun
2022	J	`jnl`	Learning Bellman Complete Representations for Offline Policy Evaluation. CoRR Jonathan D. Chang, Kaiwen Wang, Nathan Kallus, Wen Sun
2022	J	`jnl`	Provable Safe Reinforcement Learning with Binary Feedback. CoRR Andrew Bennett, Dipendra Misra, Nathan Kallus
2022	A*	`conf`	Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. NeurIPS Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
2022	J	`jnl`	Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. CoRR Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
2022	J	`jnl`	Robust and Agnostic Learning of Conditional Distributional Treatment Effects. CoRR Nathan Kallus, Miruna Oprescu
2022	J	`jnl`	Smooth Contextual Bandits: Bridging the Parametric and Nondifferentiable Regret Regimes. Oper. Res. Yichun Hu, Nathan Kallus, Xiaojie Mao
2022	A	`conf`	Stateful Offline Contextual Policy Evaluation and Learning. AISTATS Nathan Kallus, Angela Zhou
2022	A*	`conf`	The Implicit Delta Method. NeurIPS Nathan Kallus, James McInerney
2022	J	`jnl`	The Implicit Delta Method. CoRR Nathan Kallus, James McInerney
2022	—	`conf`	Treatment Effect Risk: Bounds and Inference. FAccT Nathan Kallus
2022	A*	`conf`	What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment. NeurIPS Nathan Kallus
2022	J	`jnl`	What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment. CoRR Nathan Kallus
2021	J	`jnl`	An Empirical Evaluation of the Impact of New York's Bail Reform on Crime Using Synthetic Controls. CoRR Angela Zhou, Andrew Koo, Nathan Kallus, Rene Ropac, Richard Peterson, Stephen Koppel, Tiffany Bergin
2021	J	`jnl`	Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach. CoRR Nathan Kallus, Xiaojie Mao, Masatoshi Uehara
2021	A*	`conf`	Control Variates for Slate Off-Policy Evaluation. NeurIPS Nikos Vlassis, Ashok Chandrashekar, Fernando Amat Gil, Nathan Kallus
2021	J	`jnl`	Control Variates for Slate Off-Policy Evaluation. CoRR Nikos Vlassis, Ashok Chandrashekar, Fernando Amat Gil, Nathan Kallus
2021	J	`jnl`	Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding. CoRR Jacob Dorn, Kevin Guo, Nathan Kallus
2021	—	`conf`	Fairness, Welfare, and Equity in Personalized Pricing. FAccT Nathan Kallus, Angela Zhou
2021	A*	`conf`	Fast Rates for the Regret of Offline Reinforcement Learning. COLT Yichun Hu, Nathan Kallus, Masatoshi Uehara
2021	J	`jnl`	Fast Rates for the Regret of Offline Reinforcement Learning. CoRR Yichun Hu, Nathan Kallus, Masatoshi Uehara
2021	J	`jnl`	Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency. CoRR Masatoshi Uehara, Masaaki Imaizumi, Nan Jiang, Nathan Kallus, Wen Sun, Tengyang Xie
2021	J	`jnl`	Minimax-Optimal Policy Learning Under Unobserved Confounding. Manag. Sci. Nathan Kallus, Angela Zhou
2021	A	`conf`	Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders. AISTATS Andrew Bennett, Nathan Kallus, Lihong Li, Ali Mousavi
2021	A*	`conf`	Optimal Off-Policy Evaluation from Multiple Logging Policies. ICML Nathan Kallus, Yuta Saito, Masatoshi Uehara
2021	A*	`conf`	Post-Contextual-Bandit Inference. NeurIPS Aurélien Bibaut, Maria Dimakopoulou, Nathan Kallus, Antoine Chambaz, Mark J. van der Laan
2021	J	`jnl`	Post-Contextual-Bandit Inference. CoRR Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou, Nathan Kallus, Mark J. van der Laan
2021	J	`jnl`	Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes. CoRR Andrew Bennett, Nathan Kallus
2021	J	`jnl`	Residual Overfit Method of Exploration. CoRR James McInerney, Nathan Kallus
2021	A*	`conf`	Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning. NeurIPS Aurélien Bibaut, Nathan Kallus, Maria Dimakopoulou, Antoine Chambaz, Mark J. van der Laan
2021	J	`jnl`	Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning. CoRR Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou, Nathan Kallus, Mark J. van der Laan
2021	J	`jnl`	Stateful Offline Contextual Policy Evaluation and Learning. CoRR Nathan Kallus, Angela Zhou
2020	—	`conf`	Assessing algorithmic fairness with unobserved protected class using data combination. FAT* Nathan Kallus, Xiaojie Mao, Angela Zhou
2020	J	`jnl`	Comment: Entropy Learning for Dynamic Treatment Regimes. CoRR Nathan Kallus
2020	A*	`conf`	Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning. NeurIPS Nathan Kallus, Angela Zhou
2020	J	`jnl`	Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning. CoRR Nathan Kallus, Angela Zhou
2020	J	`jnl`	DTR Bandit: Learning to Make Response-Adaptive Decisions With Low Regret. CoRR Yichun Hu, Nathan Kallus
2020	A*	`conf`	DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training. ICML Nathan Kallus
2020	J	`jnl`	Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes. J. Mach. Learn. Res. Nathan Kallus, Masatoshi Uehara
2020	A*	`conf`	Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation. ICML Nathan Kallus, Masatoshi Uehara
2020	A*	`conf`	Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies. NeurIPS Nathan Kallus, Masatoshi Uehara
2020	J	`jnl`	Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies. CoRR Nathan Kallus, Masatoshi Uehara
2020	J	`jnl`	Dynamic Assortment Personalization in High Dimensions. Oper. Res. Nathan Kallus, Madeleine Udell
2020	J	`jnl`	Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning. CoRR Nathan Kallus, Masatoshi Uehara
2020	A*	`conf`	Efficient Policy Learning from Surrogate-Loss Classification Reductions. ICML Andrew Bennett, Nathan Kallus
2020	J	`jnl`	Efficient Policy Learning from Surrogate-Loss Classification Reductions. CoRR Andrew Bennett, Nathan Kallus
2020	J	`jnl`	Fairness, Welfare, and Equity in Personalized Pricing. CoRR Nathan Kallus, Angela Zhou
2020	J	`jnl`	Fast Rates for Contextual Linear Optimization. CoRR Yichun Hu, Nathan Kallus, Xiaojie Mao
2020	J	`jnl`	From Predictive to Prescriptive Analytics. Manag. Sci. Dimitris Bertsimas, Nathan Kallus
2020	J	`jnl`	Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects. CoRR Fredrik D. Johansson, Uri Shalit, Nathan Kallus, David A. Sontag
2020	J	`jnl`	Generalized Optimal Matching Methods for Causal Inference. J. Mach. Learn. Res. Nathan Kallus
2020	J	`jnl`	Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders. CoRR Andrew Bennett, Nathan Kallus, Lihong Li, Ali Mousavi
2020	J	`jnl`	On the role of surrogates in the efficient estimation of treatment effects with limited outcome data. CoRR Nathan Kallus, Xiaojie Mao
2020	J	`jnl`	Optimal Off-Policy Evaluation from Multiple Logging Policies. CoRR Nathan Kallus, Yuta Saito, Masatoshi Uehara
2020	J	`jnl`	Rejoinder: New Objectives for Policy Learning. CoRR Nathan Kallus
2020	A*	`conf`	Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes. COLT Yichun Hu, Nathan Kallus, Xiaojie Mao
2020	A*	`conf`	Statistically Efficient Off-Policy Policy Gradients. ICML Nathan Kallus, Masatoshi Uehara
2020	J	`jnl`	Statistically Efficient Off-Policy Policy Gradients. CoRR Nathan Kallus, Masatoshi Uehara
2020	J	`jnl`	Stochastic Optimization Forests. CoRR Nathan Kallus, Xiaojie Mao
2020	J	`jnl`	The Variational Method of Moments. CoRR Andrew Bennett, Nathan Kallus
2019	J	`jnl`	Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination. CoRR Nathan Kallus, Xiaojie Mao, Angela Zhou
2019	A*	`conf`	Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds. NeurIPS Nathan Kallus, Angela Zhou
2019	J	`jnl`	Assessing Disparate Impacts of Personalized Interventions: Identifiability and Bounds. CoRR Nathan Kallus, Angela Zhou
2019	A*	`conf`	Classifying Treatment Responders Under Causal Effect Monotonicity. ICML Nathan Kallus
2019	J	`jnl`	Classifying Treatment Responders Under Causal Effect Monotonicity. CoRR Nathan Kallus
2019	J	`jnl`	Data-Pooling in Stochastic Optimization. CoRR Vishal Gupta, Nathan Kallus
2019	A*	`conf`	Deep Generalized Method of Moments for Instrumental Variable Analysis. NeurIPS Andrew Bennett, Nathan Kallus, Tobias Schnabel
2019	J	`jnl`	Deep Generalized Method of Moments for Instrumental Variable Analysis. CoRR Andrew Bennett, Nathan Kallus, Tobias Schnabel
2019	J	`jnl`	Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes. CoRR Nathan Kallus, Masatoshi Uehara
2019	J	`jnl`	Efficiently Breaking the Curse of Horizon: Double Reinforcement Learning in Infinite-Horizon Processes. CoRR Nathan Kallus, Masatoshi Uehara
2019	—	`conf`	Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved. FAT Jiahao Chen, Nathan Kallus, Xiaojie Mao, Geoffry Svacha, Madeleine Udell
2019	A	`conf`	Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding. AISTATS Nathan Kallus, Xiaojie Mao, Angela Zhou
2019	A*	`conf`	Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning. NeurIPS Nathan Kallus, Masatoshi Uehara
2019	J	`jnl`	Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning. CoRR Nathan Kallus, Masatoshi Uehara
2019	J	`jnl`	Localized Debiased Machine Learning: Efficient Estimation of Quantile Treatment Effects, Conditional Value at Risk, and Beyond. CoRR Nathan Kallus, Xiaojie Mao, Masatoshi Uehara
2019	J	`jnl`	More Efficient Policy Learning via Optimal Retargeting. CoRR Nathan Kallus
2019	A*	`conf`	Policy Evaluation with Latent Confounders via Optimal Balance. NeurIPS Andrew Bennett, Nathan Kallus
2019	J	`jnl`	Policy Evaluation with Latent Confounders via Optimal Balance. CoRR Andrew Bennett, Nathan Kallus
2019	J	`jnl`	Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes. CoRR Yichun Hu, Nathan Kallus, Xiaojie Mao
2019	A*	`conf`	The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric. NeurIPS Nathan Kallus, Angela Zhou
2019	J	`jnl`	The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric. CoRR Nathan Kallus, Angela Zhou
2018	A*	`conf`	Balanced Policy Evaluation and Learning. NeurIPS Nathan Kallus
2018	A*	`conf`	Causal Inference with Noisy and Missing Covariates via Matrix Factorization. NeurIPS Nathan Kallus, Xiaojie Mao, Madeleine Udell
2018	J	`jnl`	Causal Inference with Noisy and Missing Covariates via Matrix Factorization. CoRR Nathan Kallus, Xiaojie Mao, Madeleine Udell
2018	A*	`conf`	Confounding-Robust Policy Improvement. NeurIPS Nathan Kallus, Angela Zhou
2018	J	`jnl`	Confounding-Robust Policy Improvement. CoRR Nathan Kallus, Angela Zhou
2018	J	`jnl`	Data-driven robust optimization. Math. Program. Dimitris Bertsimas, Vishal Gupta, Nathan Kallus
2018	B	`conf`	Instrument-Armed Bandits. ALT Nathan Kallus
2018	J	`jnl`	Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding. CoRR Nathan Kallus, Xiaojie Mao, Angela Zhou
2018	A	`conf`	Policy Evaluation and Optimization with Continuous Treatments. AISTATS Nathan Kallus, Angela Zhou
2018	J	`jnl`	Policy Evaluation and Optimization with Continuous Treatments. CoRR Nathan Kallus, Angela Zhou
2018	A*	`conf`	Removing Hidden Confounding by Experimental Grounding. NeurIPS Nathan Kallus, Aahlad Manas Puli, Uri Shalit
2018	J	`jnl`	Removing Hidden Confounding by Experimental Grounding. CoRR Nathan Kallus, Aahlad Manas Puli, Uri Shalit
2018	A*	`conf`	Residual Unfairness in Fair Machine Learning from Prejudiced Data. ICML Nathan Kallus, Angela Zhou
2018	J	`jnl`	Residual Unfairness in Fair Machine Learning from Prejudiced Data. CoRR Nathan Kallus, Angela Zhou
2018	J	`jnl`	Robust sample average approximation. Math. Program. Dimitris Bertsimas, Vishal Gupta, Nathan Kallus
2017	A	`conf`	A Framework for Optimal Matching for Causal Inference. AISTATS Nathan Kallus
2017	J	`jnl`	Balanced Policy Evaluation and Learning. CoRR Nathan Kallus
2017	J	`jnl`	Instrument-Armed Bandits. CoRR Nathan Kallus
2017	A*	`conf`	Recursive Partitioning for Personalization using Observational Data. ICML Nathan Kallus
2016	—	`conf`	Causal Inference by Minimizing the Dual Norm of Bias: Kernel Matching & Weighting Estimators for Causal Effects. CFA@UAI Nathan Kallus
2016	A*	`conf`	Revealed Preference at Scale: Learning Personalized Preferences from Assortment Choices. EC Nathan Kallus, Madeleine Udell
2015	J	`jnl`	Learning Preferences from Assortment Choices in a Heterogeneous Population. CoRR Nathan Kallus, Madeleine Udell
2015	J	`jnl`	The Power of Optimization Over Randomization in Designing Experiments Involving Small Samples. Oper. Res. Dimitris Bertsimas, Mac Johnson, Nathan Kallus
2014	J	`jnl`	From Predictions to Data-Driven Decisions Using Machine Learning. CoRR Nathan Kallus
2014	—	`conf`	On the Predictive Power of Web Intelligence and Social Media - The Best Way to Predict the Future Is to tweet It. MSM/MUSE/SenseML Nathan Kallus
2014	J	`jnl`	Predicting Crowd Behavior with Big Public Data. CoRR Nathan Kallus
2014	—	`conf`	Predicting crowd behavior with big public data. WWW (Companion Volume) Nathan Kallus