Masatoshi Uehara

Adding Conditional Control to Diffusion Models with Reinforcement Learning ICLR 2025 Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design ICLR 2025 Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design ICML 2025 Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion Models NeurIPS 2024 Feedback Efficient Online Fine-Tuning of Diffusion Models ICML 2024 Functional Graphical Models: Structure Enables Offline Data-Driven Optimization AISTATS 2024 Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond JMLR 2024 Provable Offline Preference-Based Reinforcement Learning ICLR 2024 Provable Reward-Agnostic Preference-Based Reinforcement Learning ICLR 2024 Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings ICML 2023 Distributional Offline Policy Evaluation with Predictive Error Guarantees ICML 2023 Future-Dependent Value-Based Off-Policy Evaluation in POMDPs NeurIPS 2023 Inference on Strongly Identified Functionals of Weakly Identified Functions COLT 2023 Minimax Instrumental Variable Regression and L_2 Convergence Guarantees without Identification or Closedness COLT 2023 Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage NeurIPS 2023 PAC Reinforcement Learning for Predictive State Representations ICLR 2023 A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes ICML 2022 Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach ICML 2022 Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage ICLR 2022 Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems NeurIPS 2022 Representation Learning for Online and Offline RL in Low-rank MDPs ICLR 2022 Fast Rates for the Regret of Offline Reinforcement Learning COLT 2021 Information criteria for non-normalized models JMLR 2021 Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage NeurIPS 2021 Optimal Off-Policy Evaluation from Multiple Logging Policies ICML 2021 A Unified Statistically Efficient Estimation Framework for Unnormalized Models AISTATS 2020 Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation ICML 2020 Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes JMLR 2020 Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies NeurIPS 2020 Imputation estimators for unnormalized models with missing data AISTATS 2020 Minimax Weight and Q-Function Learning for Off-Policy Evaluation ICML 2020 Off-Policy Evaluation and Learning for External Validity under a Covariate Shift NeurIPS 2020 Statistically Efficient Off-Policy Policy Gradients ICML 2020 Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning NeurIPS 2019