Analysis of Noise Contrastive Estimation from the Perspective of Asymptotic Variance. - Masatoshi Uehara; Takeru Matsuda; Fumiyasu Komaki (CoRR, 2018)

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

Unified estimation framework for unnormalized models with statistical efficiency. - Masatoshi Uehara; Takafumi Kanamori; Takashi Takenouchi; Takeru Matsuda (CoRR, 2019)

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

Minimax Weight and Q-Function Learning for Off-Policy Evaluation. - Masatoshi Uehara; Nan Jiang 008 (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)

A Unified Statistically Efficient Estimation Framework for Unnormalized Models. - Masatoshi Uehara; Takafumi Kanamori; Takashi Takenouchi; Takeru Matsuda (AISTATS, 2020)

Imputation estimators for unnormalized models with missing data. - Masatoshi Uehara; Takeru Matsuda; Jae Kwang Kim (AISTATS, 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)

Minimax Weight and Q-Function Learning for Off-Policy Evaluation. - Masatoshi Uehara; Jiawei Huang; Nan Jiang 008 (ICML, 2020)

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

Off-Policy Evaluation and Learning for External Validity under a Covariate Shift. - Masatoshi Uehara; Masahiro Kato; Shota Yasui (NeurIPS, 2020)

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

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

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

Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage. - Jonathan D. Chang; Masatoshi Uehara; Dhruv Sreenivas; Rahul Kidambi; Wen Sun 002 (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)

Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage. - Jonathan D. Chang; Masatoshi Uehara; Dhruv Sreenivas; Rahul Kidambi; Wen Sun 002 (CoRR, 2021)

Pessimistic Model-based Offline RL: PAC Bounds and Posterior Sampling under Partial Coverage. - Masatoshi Uehara; Wen Sun 002 (CoRR, 2021)

A Minimax Learning Approach to Off-Policy Evaluation in Partially Observable Markov Decision Processes. - Chengchun Shi; Masatoshi Uehara; Nan Jiang 008 (CoRR, 2021)

Information criteria for non-normalized models. - Takeru Matsuda; Masatoshi Uehara; Aapo Hyvärinen (J. Mach. Learn. Res., 2021)

Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage. - Masatoshi Uehara; Wen Sun 002 (ICLR, 2022)

Representation Learning for Online and Offline RL in Low-rank MDPs. - Masatoshi Uehara; Xuezhou Zhang; Wen Sun 002 (ICLR, 2022)

A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes. - Chengchun Shi; Masatoshi Uehara; Jiawei Huang; Nan Jiang 008 (ICML, 2022)

Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach. - Xuezhou Zhang; Yuda Song 001; Masatoshi Uehara; Mengdi Wang; Alekh Agarwal; Wen Sun 002 (ICML, 2022)

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

Optimal Fixed-Budget Best Arm Identification using the Augmented Inverse Probability Weighting Estimator in Two-Armed Gaussian Bandits with Unknown Variances. - Masahiro Kato; Kaito Ariu; Masaaki Imaizumi; Masatoshi Uehara; Masahiro Nomura; Chao Qin (CoRR, 2022)

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

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

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)

PAC Reinforcement Learning for Predictive State Representations. - Wenhao Zhan; Masatoshi Uehara; Wen Sun 002; Jason D. Lee (ICLR, 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)

Distributional Offline Policy Evaluation with Predictive Error Guarantees. - Runzhe Wu; Masatoshi Uehara; Wen Sun 002 (ICML, 2023)

Off-Policy Evaluation of Ranking Policies under Diverse User Behavior. - Haruka Kiyohara; Masatoshi Uehara; Yusuke Narita; Nobuyuki Shimizu; Yasuo Yamamoto; Yuta Saito (KDD, 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)

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)

How to Query Human Feedback Efficiently in RL? - Wenhao Zhan; Masatoshi Uehara; Wen Sun 002; Jason D. Lee (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)

Functional Graphical Models: Structure Enables Offline Data-Driven Optimization. - Kuba Grudzien Kuba; Masatoshi Uehara; Sergey Levine; Pieter Abbeel (AISTATS, 2024)

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

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

Feedback Efficient Online Fine-Tuning of Diffusion Models. - Masatoshi Uehara; Yulai Zhao 002; Kevin Black; Ehsan Hajiramezanali; Gabriele Scalia; Nathaniel Lee Diamant; Alex M. Tseng; Sergey Levine; Tommaso Biancalani (ICML, 2024)

Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion Models. - Masatoshi Uehara; Yulai Zhao 002; Ehsan Hajiramezanali; Gabriele Scalia; Gökcen Eraslan; Avantika Lal; Sergey Levine; Tommaso Biancalani (NeurIPS, 2024)

Functional Graphical Models: Structure Enables Offline Data-Driven Optimization. - Jakub Grudzien Kuba; Masatoshi Uehara; Pieter Abbeel; Sergey Levine (CoRR, 2024)

Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control. - Masatoshi Uehara; Yulai Zhao 002; Kevin Black; Ehsan Hajiramezanali; Gabriele Scalia; Nathaniel Lee Diamant; Alex M. Tseng; Tommaso Biancalani; Sergey Levine (CoRR, 2024)

Regularized DeepIV with Model Selection. - Zihao Li; Hui Lan; Vasilis Syrgkanis; Mengdi Wang; Masatoshi Uehara (CoRR, 2024)

Adding Conditional Control to Diffusion Models with Reinforcement Learning. - Yulai Zhao 002; Masatoshi Uehara; Gabriele Scalia; Tommaso Biancalani; Sergey Levine; Ehsan Hajiramezanali (CoRR, 2024)

Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review. - Masatoshi Uehara; Yulai Zhao 002; Tommaso Biancalani; Sergey Levine (CoRR, 2024)

Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding. - Xiner Li; Yulai Zhao 002; Chenyu Wang 003; Gabriele Scalia; Gökcen Eraslan; Surag Nair; Tommaso Biancalani; Aviv Regev; Sergey Levine; Masatoshi Uehara (CoRR, 2024)

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

Adding Conditional Control to Diffusion Models with Reinforcement Learning. - Yulai Zhao 002; Masatoshi Uehara; Gabriele Scalia; Sun-Yuan Kung; Tommaso Biancalani; Sergey Levine; Ehsan Hajiramezanali (ICLR, 2025)

Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design. - Chenyu Wang 003; Masatoshi Uehara; Yichun He; Amy Wang; Avantika Lal; Tommi S. Jaakkola; Sergey Levine; Aviv Regev; Hanchen Wang 002; Tommaso Biancalani (ICLR, 2025)

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

Inference-Time Alignment in Diffusion Models with Reward-Guided Generation: Tutorial and Review. - Masatoshi Uehara; Yulai Zhao 002; Chenyu Wang 003; Xiner Li; Aviv Regev; Sergey Levine; Tommaso Biancalani (CoRR, 2025)

Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design. - Masatoshi Uehara; Xingyu Su; Yulai Zhao 002; Xiner Li; Aviv Regev; Shuiwang Ji; Sergey Levine; Tommaso Biancalani (CoRR, 2025)

Dynamic Search for Inference-Time Alignment in Diffusion Models. - Xiner Li; Masatoshi Uehara; Xingyu Su; Gabriele Scalia; Tommaso Biancalani; Aviv Regev; Sergey Levine; Shuiwang Ji (CoRR, 2025)

Iterative Distillation for Reward-Guided Fine-Tuning of Diffusion Models in Biomolecular Design. - Xingyu Su; Xiner Li; Masatoshi Uehara; Sunwoo Kim; Yulai Zhao 002; Gabriele Scalia; Ehsan Hajiramezanali; Tommaso Biancalani; Degui Zhi; Shuiwang Ji (CoRR, 2025)