| 2026 | WWW | Exploration Sizing via Model-Predictive Control. | Ethan Che, Hakan Ceylan, James McInerney, Nathan Kallus |
| 2025 | AISTATS | Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed Bandits. | Brian M. Cho, Dominik Meier, Kyra Gan, Nathan Kallus |
| 2025 | AISTATS | Anytime-Valid A/B Testing of Counting Processes. | Michael Lindon, Nathan Kallus |
| 2025 | AISTATS | Variation Due to Regularization Tractably Recovers Bayesian Deep Learning Uncertainty. | James McInerney, Nathan Kallus |
| 2025 | EMNLP | LLM-based Conversational Recommendation Agents with Collaborative Verbalized Experience. | Yaochen Zhu, Harald Steck, Dawen Liang, Yinhan He, Nathan Kallus, Jundong Li |
| 2025 | ICML | Multi-Armed Bandits with Interference: Bridging Causal Inference and Adversarial Bandits. | Su Jia, Peter I. Frazier, Nathan Kallus |
| 2025 | ICML | A Reductions Approach to Risk-Sensitive Reinforcement Learning with Optimized Certainty Equivalents. | Kaiwen Wang, Dawen Liang, Nathan Kallus, Wen Sun |
| 2025 | KDD | 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 |
| 2025 | KDD | Evaluating Decision Rules Across Many Weak Experiments. | Winston Chou, Colin Gray, Nathan Kallus, Aurlien Bibaut, Simon Ejdemyr |
| 2025 | WWW | Does Weighting Improve Matrix Factorization for Recommender Systems? | Alex Ayoub, Samuel Robertson, Dawen Liang, Harald Steck, Nathan Kallus |
| 2025 | WWW | Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems. | Yaochen Zhu, Chao Wan, Harald Steck, Dawen Liang, Yesu Feng, Nathan Kallus, Jundong Li |
| 2025 | WSDM | 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 |
| 2024 | AISTATS | Low-rank MDPs with Continuous Action Spaces. | Miruna Oprescu, Andrew Bennett, Nathan Kallus |
| 2024 | ICLR | Provable Offline Preference-Based Reinforcement Learning. | Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun |
| 2024 | ICML | Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams. | Brian Cho, Kyra Gan, Nathan Kallus |
| 2024 | ICML | 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 Szepesvri |
| 2024 | ICML | Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments. | Allen Tran, Aurlien Bibaut, Nathan Kallus |
| 2024 | ICML | More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning. | Kaiwen Wang, Owen Oertell, Alekh Agarwal, Nathan Kallus, Wen Sun |
| 2024 | KDD | Learning the Covariance of Treatment Effects Across Many Weak Experiments. | Aurlien Bibaut, Winston Chou, Simon Ejdemyr, Nathan Kallus |
| 2024 | RecSys | 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 |
| 2024 | WWW | Off-Policy Evaluation for Large Action Spaces via Policy Convolution. | Noveen Sachdeva, Lequn Wang, Dawen Liang, Nathan Kallus, Julian J. McAuley |
| 2024 | WWW | Is Cosine-Similarity of Embeddings Really About Similarity? | Harald Steck, Chaitanya Ekanadham, Nathan Kallus |
| 2023 | AISTATS | Provable Safe Reinforcement Learning with Binary Feedback. | Andrew Bennett, Dipendra Misra, Nathan Kallus |
| 2023 | AISTATS | Robust and Agnostic Learning of Conditional Distributional Treatment Effects. | Nathan Kallus, Miruna Oprescu |
| 2023 | CIKM | 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 |
| 2023 | COLT | Inference on Strongly Identified Functionals of Weakly Identified Functions. | Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara |
| 2023 | COLT | Minimax Instrumental Variable Regression and L | Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara |
| 2023 | ICML | Smooth Non-stationary Bandits. | Su Jia, Qian Xie, Nathan Kallus, Peter I. Frazier |
| 2023 | ICML | B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding. | Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit |
| 2023 | ICML | Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. | Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun |
| 2023 | ICML | Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR. | Kaiwen Wang, Nathan Kallus, Wen Sun |
| 2022 | AISTATS | Stateful Offline Contextual Policy Evaluation and Learning. | Nathan Kallus, Angela Zhou |
| 2022 | CVPR | Estimating Structural Disparities for Face Models. | Shervin Ardeshir, Cristina Segalin, Nathan Kallus |
| 2022 | ICML | Learning Bellman Complete Representations for Offline Policy Evaluation. | Jonathan D. Chang, Kaiwen Wang, Nathan Kallus, Wen Sun |
| 2022 | ICML | Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning. | Nathan Kallus, Xiaojie Mao, Kaiwen Wang, Zhengyuan Zhou |
| 2021 | AISTATS | Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders. | Andrew Bennett, Nathan Kallus, Lihong Li, Ali Mousavi |
| 2021 | COLT | Fast Rates for the Regret of Offline Reinforcement Learning. | Yichun Hu, Nathan Kallus, Masatoshi Uehara |
| 2021 | ICML | Optimal Off-Policy Evaluation from Multiple Logging Policies. | Nathan Kallus, Yuta Saito, Masatoshi Uehara |
| 2020 | COLT | Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes. | Yichun Hu, Nathan Kallus, Xiaojie Mao |
| 2020 | ICML | Efficient Policy Learning from Surrogate-Loss Classification Reductions. | Andrew Bennett, Nathan Kallus |
| 2020 | ICML | DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training. | Nathan Kallus |
| 2020 | ICML | Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation. | Nathan Kallus, Masatoshi Uehara |
| 2020 | ICML | Statistically Efficient Off-Policy Policy Gradients. | Nathan Kallus, Masatoshi Uehara |
| 2019 | AISTATS | Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding. | Nathan Kallus, Xiaojie Mao, Angela Zhou |
| 2019 | ICML | Classifying Treatment Responders Under Causal Effect Monotonicity. | Nathan Kallus |
| 2018 | AISTATS | Policy Evaluation and Optimization with Continuous Treatments. | Nathan Kallus, Angela Zhou |
| 2018 | ALT | Instrument-Armed Bandits. | Nathan Kallus |
| 2018 | ICML | Residual Unfairness in Fair Machine Learning from Prejudiced Data. | Nathan Kallus, Angela Zhou |
| 2017 | AISTATS | A Framework for Optimal Matching for Causal Inference. | Nathan Kallus |
| 2017 | ICML | Recursive Partitioning for Personalization using Observational Data. | Nathan Kallus |
| 2016 | UAI | Causal Inference by Minimizing the Dual Norm of Bias: Kernel Matching & Weighting Estimators for Causal Effects. | Nathan Kallus |
| 2014 | WWW | Predicting crowd behavior with big public data. | Nathan Kallus |