On the Predictive Power of Web Intelligence and Social Media - The Best Way to Predict the Future Is to tweet It. - Nathan Kallus (MSM/MUSE/SenseML, 2014)

Predicting crowd behavior with big public data. - Nathan Kallus (WWW (Companion Volume), 2014)

From Predictions to Data-Driven Decisions Using Machine Learning. - Nathan Kallus (CoRR, 2014)

Learning Preferences from Assortment Choices in a Heterogeneous Population. - Nathan Kallus; Madeleine Udell (CoRR, 2015)

The Power of Optimization Over Randomization in Designing Experiments Involving Small Samples. - Dimitris Bertsimas; Mac Johnson; Nathan Kallus (Oper. Res., 2015)

Revealed Preference at Scale: Learning Personalized Preferences from Assortment Choices. - Nathan Kallus; Madeleine Udell (EC, 2016)

Causal Inference by Minimizing the Dual Norm of Bias: Kernel Matching & Weighting Estimators for Causal Effects. - Nathan Kallus (CFA@UAI, 2016)

A Framework for Optimal Matching for Causal Inference. - Nathan Kallus (AISTATS, 2017)

Recursive Partitioning for Personalization using Observational Data. - Nathan Kallus (ICML, 2017)

Policy Evaluation and Optimization with Continuous Treatments. - Nathan Kallus; Angela Zhou (AISTATS, 2018)

Instrument-Armed Bandits. - Nathan Kallus (ALT, 2018)

Residual Unfairness in Fair Machine Learning from Prejudiced Data. - Nathan Kallus; Angela Zhou (ICML, 2018)

Balanced Policy Evaluation and Learning. - Nathan Kallus (NeurIPS, 2018)

Causal Inference with Noisy and Missing Covariates via Matrix Factorization. - Nathan Kallus; Xiaojie Mao; Madeleine Udell (NeurIPS, 2018)

Removing Hidden Confounding by Experimental Grounding. - Nathan Kallus; Aahlad Manas Puli; Uri Shalit (NeurIPS, 2018)

Confounding-Robust Policy Improvement. - Nathan Kallus; Angela Zhou (NeurIPS, 2018)

Data-driven robust optimization. - Dimitris Bertsimas; Vishal Gupta 004; Nathan Kallus (Math. Program., 2018)

Robust sample average approximation. - Dimitris Bertsimas; Vishal Gupta 004; Nathan Kallus (Math. Program., 2018)

Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding. - Nathan Kallus; Xiaojie Mao; Angela Zhou (AISTATS, 2019)

Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved. - Jiahao Chen 001; Nathan Kallus; Xiaojie Mao; Geoffry Svacha; Madeleine Udell (FAT, 2019)

Classifying Treatment Responders Under Causal Effect Monotonicity. - Nathan Kallus (ICML, 2019)

Policy Evaluation with Latent Confounders via Optimal Balance. - Andrew Bennett; Nathan Kallus (NeurIPS, 2019)

Deep Generalized Method of Moments for Instrumental Variable Analysis. - Andrew Bennett; Nathan Kallus; Tobias Schnabel (NeurIPS, 2019)

Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning. - Nathan Kallus; Masatoshi Uehara (NeurIPS, 2019)

Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds. - Nathan Kallus; Angela Zhou (NeurIPS, 2019)

The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric. - Nathan Kallus; Angela Zhou (NeurIPS, 2019)

Assessing Disparate Impacts of Personalized Interventions: Identifiability and Bounds. - Nathan Kallus; Angela Zhou (CoRR, 2019)

More Efficient Policy Learning via Optimal Retargeting. - Nathan Kallus (CoRR, 2019)

Efficiently Breaking the Curse of Horizon: Double Reinforcement Learning in Infinite-Horizon Processes. - Nathan Kallus; Masatoshi Uehara (CoRR, 2019)

Localized Debiased Machine Learning: Efficient Estimation of Quantile Treatment Effects, Conditional Value at Risk, and Beyond. - Nathan Kallus; Xiaojie Mao; Masatoshi Uehara (CoRR, 2019)

Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes. - Yichun Hu; Nathan Kallus; Xiaojie Mao (COLT, 2020)

Efficient Policy Learning from Surrogate-Loss Classification Reductions. - Andrew Bennett; Nathan Kallus (ICML, 2020)

DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training. - Nathan Kallus (ICML, 2020)

Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation. - Nathan Kallus; Masatoshi Uehara (ICML, 2020)

Statistically Efficient Off-Policy Policy Gradients. - Nathan Kallus; Masatoshi Uehara (ICML, 2020)

Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies. - Nathan Kallus; Masatoshi Uehara (NeurIPS, 2020)

Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning. - Nathan Kallus; Angela Zhou (NeurIPS, 2020)

On the role of surrogates in the efficient estimation of treatment effects with limited outcome data. - Nathan Kallus; Xiaojie Mao (CoRR, 2020)

Comment: Entropy Learning for Dynamic Treatment Regimes. - Nathan Kallus (CoRR, 2020)

DTR Bandit: Learning to Make Response-Adaptive Decisions With Low Regret. - Yichun Hu; Nathan Kallus (CoRR, 2020)

Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning. - Nathan Kallus; Masatoshi Uehara (CoRR, 2020)

Rejoinder: New Objectives for Policy Learning. - Nathan Kallus (CoRR, 2020)

The Variational Method of Moments. - Andrew Bennett; Nathan Kallus (CoRR, 2020)

Dynamic Assortment Personalization in High Dimensions. - Nathan Kallus; Madeleine Udell (Oper. Res., 2020)

Generalized Optimal Matching Methods for Causal Inference. - Nathan Kallus (J. Mach. Learn. Res., 2020)

Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes. - Nathan Kallus; Masatoshi Uehara (J. Mach. Learn. Res., 2020)

From Predictive to Prescriptive Analytics. - Dimitris Bertsimas; Nathan Kallus (Manag. Sci., 2020)

Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders. - Andrew Bennett; Nathan Kallus; Lihong Li 001; Ali Mousavi 003 (AISTATS, 2021)

Fairness, Welfare, and Equity in Personalized Pricing. - Nathan Kallus; Angela Zhou (FAccT, 2021)

Optimal Off-Policy Evaluation from Multiple Logging Policies. - Nathan Kallus; Yuta Saito; Masatoshi Uehara (ICML, 2021)

Post-Contextual-Bandit Inference. - Aurélien Bibaut; Maria Dimakopoulou; Nathan Kallus; Antoine Chambaz; Mark J. van der Laan (NeurIPS, 2021)

Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning. - Aurélien Bibaut; Nathan Kallus; Maria Dimakopoulou; Antoine Chambaz; Mark J. van der Laan (NeurIPS, 2021)

Control Variates for Slate Off-Policy Evaluation. - Nikos Vlassis; Ashok Chandrashekar; Fernando Amat Gil; Nathan Kallus (NeurIPS, 2021)

Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency. - Masatoshi Uehara; Masaaki Imaizumi; Nan Jiang 008; Nathan Kallus; Wen Sun 002; Tengyang Xie (CoRR, 2021)

Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach. - Nathan Kallus; Xiaojie Mao; Masatoshi Uehara (CoRR, 2021)

Residual Overfit Method of Exploration. - James McInerney; Nathan Kallus (CoRR, 2021)

An Empirical Evaluation of the Impact of New York's Bail Reform on Crime Using Synthetic Controls. - Angela Zhou; Andrew Koo; Nathan Kallus; Rene Ropac; Richard Peterson; Stephen Koppel; Tiffany Bergin (CoRR, 2021)

Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding. - Jacob Dorn; Kevin Guo; Nathan Kallus (CoRR, 2021)

Minimax-Optimal Policy Learning Under Unobserved Confounding. - Nathan Kallus; Angela Zhou (Manag. Sci., 2021)

Stateful Offline Contextual Policy Evaluation and Learning. - Nathan Kallus; Angela Zhou (AISTATS, 2022)

Estimating Structural Disparities for Face Models. - Shervin Ardeshir; Cristina Segalin; Nathan Kallus (CVPR, 2022)

Learning Bellman Complete Representations for Offline Policy Evaluation. - Jonathan D. Chang; Kaiwen Wang; Nathan Kallus; Wen Sun 002 (ICML, 2022)

Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning. - Nathan Kallus; Xiaojie Mao; Kaiwen Wang; Zhengyuan Zhou (ICML, 2022)

What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment. - Nathan Kallus (NeurIPS, 2022)

The Implicit Delta Method. - Nathan Kallus; James McInerney (NeurIPS, 2022)

Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. - Masatoshi Uehara; Ayush Sekhari; Jason D. Lee; Nathan Kallus; Wen Sun 002 (NeurIPS, 2022)

A Review of Off-Policy Evaluation in Reinforcement Learning. - Masatoshi Uehara; Chengchun Shi; Nathan Kallus (CoRR, 2022)

Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects. - Fredrik D. Johansson; Uri Shalit; Nathan Kallus; David A. Sontag (J. Mach. Learn. Res., 2022)

Data Pooling in Stochastic Optimization. - Vishal Gupta 004; Nathan Kallus (Manag. Sci., 2022)

Fast Rates for Contextual Linear Optimization. - Yichun Hu; Nathan Kallus; Xiaojie Mao (Manag. Sci., 2022)

Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination. - Nathan Kallus; Xiaojie Mao; Angela Zhou (Manag. Sci., 2022)

Smooth Contextual Bandits: Bridging the Parametric and Nondifferentiable Regret Regimes. - Yichun Hu; Nathan Kallus; Xiaojie Mao (Oper. Res., 2022)

Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning. - Nathan Kallus; Masatoshi Uehara (Oper. Res., 2022)

Provable Safe Reinforcement Learning with Binary Feedback. - Andrew Bennett; Dipendra Misra; Nathan Kallus (AISTATS, 2023)

Robust and Agnostic Learning of Conditional Distributional Treatment Effects. - Nathan Kallus; Miruna Oprescu (AISTATS, 2023)

Large Language Models as Zero-Shot Conversational Recommenders. - Zhankui He; Zhouhang Xie; Rahul Jha; Harald Steck; Dawen Liang; Yesu Feng; Bodhisattwa Prasad Majumder; Nathan Kallus; Julian J. McAuley (CIKM, 2023)

Inference on Strongly Identified Functionals of Weakly Identified Functions. - Andrew Bennett; Nathan Kallus; Xiaojie Mao; Whitney Newey; Vasilis Syrgkanis; Masatoshi Uehara (COLT, 2023)

Minimax Instrumental Variable Regression and L2 Convergence Guarantees without Identification or Closedness. - Andrew Bennett; Nathan Kallus; Xiaojie Mao; Whitney Newey; Vasilis Syrgkanis; Masatoshi Uehara (COLT, 2023)

Smooth Non-stationary Bandits. - Su Jia; Qian Xie 005; Nathan Kallus; Peter I. Frazier (ICML, 2023)

B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding. - Miruna Oprescu; Jacob Dorn; Marah Ghoummaid; Andrew Jesson; Nathan Kallus; Uri Shalit (ICML, 2023)

Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. - Masatoshi Uehara; Ayush Sekhari; Jason D. Lee; Nathan Kallus; Wen Sun 002 (ICML, 2023)

Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR. - Kaiwen Wang; Nathan Kallus; Wen Sun 002 (ICML, 2023)

Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. - Masatoshi Uehara; Haruka Kiyohara; Andrew Bennett; Victor Chernozhukov; Nan Jiang 008; Nathan Kallus; Chengchun Shi; Wen Sun 002 (NeurIPS, 2023)

Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage. - Masatoshi Uehara; Nathan Kallus; Jason D. Lee; Wen Sun 002 (NeurIPS, 2023)

The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning. - Kaiwen Wang; Kevin Zhou; Runzhe Wu; Nathan Kallus; Wen Sun 002 (NeurIPS, 2023)

Refined Value-Based Offline RL under Realizability and Partial Coverage. - Masatoshi Uehara; Nathan Kallus; Jason D. Lee; Wen Sun 002 (CoRR, 2023)

Minimax Instrumental Variable Regression and $L_2$ Convergence Guarantees without Identification or Closedness. - Andrew Bennett; Nathan Kallus; Xiaojie Mao; Whitney Newey; Vasilis Syrgkanis; Masatoshi Uehara (CoRR, 2023)

Provable Offline Reinforcement Learning with Human Feedback. - Wenhao Zhan; Masatoshi Uehara; Nathan Kallus; Jason D. Lee; Wen Sun 002 (CoRR, 2023)

JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning. - Kaiwen Wang; Junxiong Wang; Yueying Li; Nathan Kallus; Immanuel Trummer; Wen Sun 002 (CoRR, 2023)

Source Condition Double Robust Inference on Functionals of Inverse Problems. - Andrew Bennett; Nathan Kallus; Xiaojie Mao; Whitney Newey; Vasilis Syrgkanis; Masatoshi Uehara (CoRR, 2023)

Faster Rates for Switchback Experiments. - Su Jia; Sohom Bhattacharya; Nathan Kallus; Christina Lee Yu (CoRR, 2023)

The Power and Limits of Predictive Approaches to Observational Data-Driven Optimization: The Case of Pricing. - Dimitris Bertsimas; Nathan Kallus (INFORMS J. Optim., 2023)

Stochastic Optimization Forests. - Nathan Kallus; Xiaojie Mao (Manag. Sci., 2023)

Treatment Effect Risk: Bounds and Inference. - Nathan Kallus (Manag. Sci., 2023)

Low-rank MDPs with Continuous Action Spaces. - Miruna Oprescu; Andrew Bennett; Nathan Kallus (AISTATS, 2024)

Provable Offline Preference-Based Reinforcement Learning. - Wenhao Zhan; Masatoshi Uehara; Nathan Kallus; Jason D. Lee; Wen Sun 002 (ICLR, 2024)

Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams. - Brian Cho 001; Kyra Gan; Nathan Kallus (ICML, 2024)

Switching the Loss Reduces the Cost in Batch Reinforcement Learning. - Alex Ayoub; Kaiwen Wang; Vincent Liu; Samuel Robertson; James McInerney; Dawen Liang; Nathan Kallus; Csaba Szepesvári (ICML, 2024)

Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments. - Allen Tran; Aurélien Bibaut; Nathan Kallus (ICML, 2024)

More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning. - Kaiwen Wang; Owen Oertell; Alekh Agarwal; Nathan Kallus; Wen Sun 002 (ICML, 2024)

Learning the Covariance of Treatment Effects Across Many Weak Experiments. - Aurélien Bibaut; Winston Chou; Simon Ejdemyr; Nathan Kallus (KDD, 2024)

Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes. - Andrew Bennett; Nathan Kallus; Miruna Oprescu; Wen Sun 002; Kaiwen Wang (NeurIPS, 2024)

Contextual Linear Optimization with Bandit Feedback. - Yichun Hu; Nathan Kallus; Xiaojie Mao; Yanchen Wu (NeurIPS, 2024)

Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data. - Miruna Oprescu; Nathan Kallus (NeurIPS, 2024)

Neighborhood-Based Collaborative Filtering for Conversational Recommendation. - Zhouhang Xie; Junda Wu; Hyunsik Jeon; Zhankui He; Harald Steck; Rahul Jha; Dawen Liang; Nathan Kallus; Julian J. McAuley (RecSys, 2024)

JoinGym: An Efficient Join Order Selection Environment. - Junxiong Wang; Kaiwen Wang; Yueying Li; Nathan Kallus; Immanuel Trummer; Wen Sun 002 (RLJ, 2024)

Off-Policy Evaluation for Large Action Spaces via Policy Convolution. - Noveen Sachdeva; Lequn Wang; Dawen Liang; Nathan Kallus; Julian J. McAuley (WWW, 2024)

Is Cosine-Similarity of Embeddings Really About Similarity? - Harald Steck; Chaitanya Ekanadham; Nathan Kallus (WWW (Companion Volume), 2024)

Multi-Armed Bandits with Interference. - Su Jia; Peter I. Frazier; Nathan Kallus (CoRR, 2024)

Applied Causal Inference Powered by ML and AI. - Victor Chernozhukov; Christian Hansen; Nathan Kallus; Martin Spindler; Vasilis Syrgkanis (CoRR, 2024)

Risk-Sensitive RL with Optimized Certainty Equivalents via Reduction to Standard RL. - Kaiwen Wang; Dawen Liang; Nathan Kallus; Wen Sun 002 (CoRR, 2024)

Hessian-Free Laplace in Bayesian Deep Learning. - James McInerney; Nathan Kallus (CoRR, 2024)

The Central Role of the Loss Function in Reinforcement Learning. - Kaiwen Wang; Nathan Kallus; Wen Sun 002 (CoRR, 2024)

Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes. - Andrew Bennett; Nathan Kallus (Oper. Res., 2024)

Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond. - Nathan Kallus; Xiaojie Mao; Masatoshi Uehara (J. Mach. Learn. Res., 2024)

Adjusting regression models for conditional uncertainty calibration. - Ruijiang Gao; Mingzhang Yin; James McInerney; Nathan Kallus (Mach. Learn., 2024)

Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed Bandits. - Brian M. Cho; Dominik Meier; Kyra Gan; Nathan Kallus (AISTATS, 2025)

Anytime-Valid A/B Testing of Counting Processes. - Michael Lindon; Nathan Kallus (AISTATS, 2025)

Variation Due to Regularization Tractably Recovers Bayesian Deep Learning Uncertainty. - James McInerney; Nathan Kallus (AISTATS, 2025)

CSPI-MT: Calibrated Safe Policy Improvement with Multiple Testing for Threshold Policies. - Brian M. Cho; Ana-Roxana Pop; Kyra Gan; Sam Corbett-Davies; Israel Nir; Ariel Evnine; Nathan Kallus (KDD (1), 2025)

Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation. - Zhankui He; Zhouhang Xie; Harald Steck; Dawen Liang; Rahul Jha; Nathan Kallus; Julian J. McAuley (WSDM, 2025)

Does Weighting Improve Matrix Factorization for Recommender Systems? - Alex Ayoub; Samuel Robertson; Dawen Liang; Harald Steck; Nathan Kallus (WWW, 2025)

Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems. - Yaochen Zhu; Chao Wan; Harald Steck; Dawen Liang; Yesu Feng; Nathan Kallus; Jundong Li (WWW, 2025)

Fast Rates for the Regret of Offline Reinforcement Learning. - Yichun Hu; Nathan Kallus; Masatoshi Uehara (Math. Oper. Res., 2025)

Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference. - Lars van der Laan; David Hubbard; Allen Tran; Nathan Kallus; Aurélien Bibaut (CoRR, 2025)

GST-UNet: Spatiotemporal Causal Inference with Time-Varying Confounders. - Miruna Oprescu; David Keetae Park; Xihaier Luo; Shinjae Yoo; Nathan Kallus (CoRR, 2025)

Q♯: Provably Optimal Distributional RL for LLM Post-Training. - Jin Peng Zhou; Kaiwen Wang; Jonathan D. Chang; Zhaolin Gao; Nathan Kallus; Kilian Q. Weinberger; Kianté Brantley; Wen Sun 002 (CoRR, 2025)

SNPL: Simultaneous Policy Learning and Evaluation for Safe Multi-Objective Policy Improvement. - Brian M. Cho; Ana-Roxana Pop; Ariel Evnine; Nathan Kallus (CoRR, 2025)

From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System. - Rohan Surana; Junda Wu; Zhouhang Xie; Yu Xia 007; Harald Steck; Dawen Liang; Nathan Kallus; Julian J. McAuley (CoRR, 2025)

Value-Guided Search for Efficient Chain-of-Thought Reasoning. - Kaiwen Wang; Jin Peng Zhou; Jonathan D. Chang; Zhaolin Gao; Nathan Kallus; Kianté Brantley; Wen Sun 002 (CoRR, 2025)

Efficient Adaptive Experimentation with Non-Compliance. - Miruna Oprescu; Brian M. Cho; Nathan Kallus (CoRR, 2025)

Simulation-Based Inference for Adaptive Experiments. - Brian M. Cho; Aurélien Bibaut; Nathan Kallus (CoRR, 2025)

Optimization of Epsilon-Greedy Exploration. - Ethan Che; Hakan Ceylan; James McInerney; Nathan Kallus (CoRR, 2025)