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Masatoshi Uehara
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2020 – today
- 2024
- [j4]Nathan Kallus, Xiaojie Mao, Masatoshi Uehara:
Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond. J. Mach. Learn. Res. 25: 16:1-16:59 (2024) - [c28]Kuba Grudzien Kuba, Masatoshi Uehara, Sergey Levine, Pieter Abbeel:
Functional Graphical Models: Structure Enables Offline Data-Driven Optimization. AISTATS 2024: 2449-2457 - [c27]Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee:
Provable Reward-Agnostic Preference-Based Reinforcement Learning. ICLR 2024 - [c26]Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Provable Offline Preference-Based Reinforcement Learning. ICLR 2024 - [c25]Masatoshi Uehara, Yulai Zhao, Kevin Black, Ehsan Hajiramezanali, Gabriele Scalia, Nathaniel Lee Diamant, Alex M. Tseng, Sergey Levine, Tommaso Biancalani:
Feedback Efficient Online Fine-Tuning of Diffusion Models. ICML 2024 - [i43]Jakub Grudzien Kuba, Masatoshi Uehara, Pieter Abbeel, Sergey Levine:
Functional Graphical Models: Structure Enables Offline Data-Driven Optimization. CoRR abs/2401.05442 (2024) - [i42]Masatoshi Uehara, Yulai Zhao, Kevin Black, Ehsan Hajiramezanali, Gabriele Scalia, Nathaniel Lee Diamant, Alex M. Tseng, Tommaso Biancalani, Sergey Levine:
Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control. CoRR abs/2402.15194 (2024) - [i41]Masatoshi Uehara, Yulai Zhao, Kevin Black, Ehsan Hajiramezanali, Gabriele Scalia, Nathaniel Lee Diamant, Alex M. Tseng, Sergey Levine, Tommaso Biancalani:
Feedback Efficient Online Fine-Tuning of Diffusion Models. CoRR abs/2402.16359 (2024) - [i40]Zihao Li, Hui Lan, Vasilis Syrgkanis, Mengdi Wang
, Masatoshi Uehara:
Regularized DeepIV with Model Selection. CoRR abs/2403.04236 (2024) - [i39]Masatoshi Uehara, Yulai Zhao, Ehsan Hajiramezanali, Gabriele Scalia, Gökcen Eraslan, Avantika Lal, Sergey Levine, Tommaso Biancalani:
Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion Models. CoRR abs/2405.19673 (2024) - [i38]Yulai Zhao, Masatoshi Uehara, Gabriele Scalia, Tommaso Biancalani, Sergey Levine, Ehsan Hajiramezanali:
Adding Conditional Control to Diffusion Models with Reinforcement Learning. CoRR abs/2406.12120 (2024) - [i37]Masatoshi Uehara, Yulai Zhao, Tommaso Biancalani, Sergey Levine:
Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review. CoRR abs/2407.13734 (2024) - [i36]Xiner Li, Yulai Zhao, Chenyu Wang, Gabriele Scalia, Gökcen Eraslan, Surag Nair, Tommaso Biancalani, Aviv Regev, Sergey Levine, Masatoshi Uehara:
Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding. CoRR abs/2408.08252 (2024) - 2023
- [c24]Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara:
Inference on Strongly Identified Functionals of Weakly Identified Functions. COLT 2023: 2265 - [c23]Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara:
Minimax Instrumental Variable Regression and L2 Convergence Guarantees without Identification or Closedness. COLT 2023: 2291-2318 - [c22]Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee:
PAC Reinforcement Learning for Predictive State Representations. ICLR 2023 - [c21]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. ICML 2023: 34615-34641 - [c20]Runzhe Wu, Masatoshi Uehara, Wen Sun:
Distributional Offline Policy Evaluation with Predictive Error Guarantees. ICML 2023: 37685-37712 - [c19]Haruka Kiyohara
Off-Policy Evaluation of Ranking Policies under Diverse User Behavior. KDD 2023: 1154-1163 - [c18]Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang, Nathan Kallus, Chengchun Shi, Wen Sun:
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. NeurIPS 2023 - [c17]Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage. NeurIPS 2023 - [i35]Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Refined Value-Based Offline RL under Realizability and Partial Coverage. CoRR abs/2302.02392 (2023) - [i34]Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara:
Minimax Instrumental Variable Regression and $L_2$ Convergence Guarantees without Identification or Closedness. CoRR abs/2302.05404 (2023) - [i33]Runzhe Wu, Masatoshi Uehara, Wen Sun:
Distributional Offline Policy Evaluation with Predictive Error Guarantees. CoRR abs/2302.09456 (2023) - [i32]Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun:
Provable Offline Reinforcement Learning with Human Feedback. CoRR abs/2305.14816 (2023) - [i31]Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee:
How to Query Human Feedback Efficiently in RL? CoRR abs/2305.18505 (2023) - [i30]Haruka Kiyohara, Masatoshi Uehara, Yusuke Narita, Nobuyuki Shimizu, Yasuo Yamamoto, Yuta Saito:
Off-Policy Evaluation of Ranking Policies under Diverse User Behavior. CoRR abs/2306.15098 (2023) - [i29]Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara:
Source Condition Double Robust Inference on Functionals of Inverse Problems. CoRR abs/2307.13793 (2023) - 2022
- [j3]Nathan Kallus
Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning. Oper. Res. 70(6): 3282-3302 (2022) - [c16]Masatoshi Uehara, Wen Sun:
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage. ICLR 2022 - [c15]Masatoshi Uehara, Xuezhou Zhang, Wen Sun:
Representation Learning for Online and Offline RL in Low-rank MDPs. ICLR 2022 - [c14]Chengchun Shi, Masatoshi Uehara, Jiawei Huang, Nan Jiang:
A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes. ICML 2022: 20057-20094 - [c13]Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun:
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach. ICML 2022: 26517-26547 - [c12]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. NeurIPS 2022 - [i28]Masahiro Kato, Kaito Ariu, Masaaki Imaizumi, Masatoshi Uehara, Masahiro Nomura, Chao Qin:
Optimal Fixed-Budget Best Arm Identification using the Augmented Inverse Probability Weighting Estimator in Two-Armed Gaussian Bandits with Unknown Variances. CoRR abs/2201.04469 (2022) - [i27]Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun:
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach. CoRR abs/2202.00063 (2022) - [i26]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. CoRR abs/2206.12020 (2022) - [i25]Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun:
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. CoRR abs/2206.12081 (2022) - [i24]Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee:
PAC Reinforcement Learning for Predictive State Representations. CoRR abs/2207.05738 (2022) - [i23]Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang, Nathan Kallus, Chengchun Shi, Wen Sun:
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. CoRR abs/2207.13081 (2022) - [i22]Masatoshi Uehara, Chengchun Shi, Nathan Kallus:
A Review of Off-Policy Evaluation in Reinforcement Learning. CoRR abs/2212.06355 (2022) - 2021
- [j2]Takeru Matsuda, Masatoshi Uehara, Aapo Hyvärinen:
Information criteria for non-normalized models. J. Mach. Learn. Res. 22: 158:1-158:33 (2021) - [c11]Yichun Hu, Nathan Kallus, Masatoshi Uehara:
Fast Rates for the Regret of Offline Reinforcement Learning. COLT 2021: 2462 - [c10]Nathan Kallus, Yuta Saito, Masatoshi Uehara:
Optimal Off-Policy Evaluation from Multiple Logging Policies. ICML 2021: 5247-5256 - [c9]Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun:
Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage. NeurIPS 2021: 965-979 - [i21]Yichun Hu, Nathan Kallus, Masatoshi Uehara:
Fast Rates for the Regret of Offline Reinforcement Learning. CoRR abs/2102.00479 (2021) - [i20]Masatoshi Uehara, Masaaki Imaizumi, Nan Jiang, Nathan Kallus, Wen Sun, Tengyang Xie:
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency. CoRR abs/2102.02981 (2021) - [i19]Nathan Kallus, Xiaojie Mao, Masatoshi Uehara:
Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach. CoRR abs/2103.14029 (2021) - [i18]Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun:
Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage. CoRR abs/2106.03207 (2021) - [i17]Masatoshi Uehara, Wen Sun:
Pessimistic Model-based Offline RL: PAC Bounds and Posterior Sampling under Partial Coverage. CoRR abs/2107.06226 (2021) - [i16]Masatoshi Uehara, Xuezhou Zhang, Wen Sun:
Representation Learning for Online and Offline RL in Low-rank MDPs. CoRR abs/2110.04652 (2021) - [i15]Chengchun Shi, Masatoshi Uehara, Nan Jiang:
A Minimax Learning Approach to Off-Policy Evaluation in Partially Observable Markov Decision Processes. CoRR abs/2111.06784 (2021) - 2020
- [j1]Nathan Kallus, Masatoshi Uehara:
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes. J. Mach. Learn. Res. 21: 167:1-167:63 (2020) - [c8]Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi, Takeru Matsuda:
A Unified Statistically Efficient Estimation Framework for Unnormalized Models. AISTATS 2020: 809-819 - [c7]Masatoshi Uehara, Takeru Matsuda, Jae Kwang Kim:
Imputation estimators for unnormalized models with missing data. AISTATS 2020: 831-841 - [c6]Nathan Kallus, Masatoshi Uehara:
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation. ICML 2020: 5078-5088 - [c5]Nathan Kallus, Masatoshi Uehara:
Statistically Efficient Off-Policy Policy Gradients. ICML 2020: 5089-5100 - [c4]Masatoshi Uehara, Jiawei Huang, Nan Jiang:
Minimax Weight and Q-Function Learning for Off-Policy Evaluation. ICML 2020: 9659-9668 - [c3]Nathan Kallus, Masatoshi Uehara:
Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies. NeurIPS 2020 - [c2]Masatoshi Uehara, Masahiro Kato, Shota Yasui:
Off-Policy Evaluation and Learning for External Validity under a Covariate Shift. NeurIPS 2020 - [i14]Nathan Kallus, Masatoshi Uehara:
Statistically Efficient Off-Policy Policy Gradients. CoRR abs/2002.04014 (2020) - [i13]Masahiro Kato, Masatoshi Uehara, Shota Yasui:
Off-Policy Evaluation and Learning for External Validity under a Covariate Shift. CoRR abs/2002.11642 (2020) - [i12]Nathan Kallus, Masatoshi Uehara:
Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning. CoRR abs/2006.03886 (2020) - [i11]Nathan Kallus, Masatoshi Uehara:
Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies. CoRR abs/2006.03900 (2020) - [i10]Nathan Kallus, Yuta Saito, Masatoshi Uehara:
Optimal Off-Policy Evaluation from Multiple Logging Policies. CoRR abs/2010.11002 (2020)
2010 – 2019
- 2019
- [c1]Nathan Kallus, Masatoshi Uehara:
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning. NeurIPS 2019: 3320-3329 - [i9]Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi, Takeru Matsuda:
Unified estimation framework for unnormalized models with statistical efficiency. CoRR abs/1901.07710 (2019) - [i8]Masatoshi Uehara, Takeru Matsuda, Jae Kwang Kim:
Imputation estimators for unnormalized models with missing data. CoRR abs/1903.03630 (2019) - [i7]Takeru Matsuda, Masatoshi Uehara, Aapo Hyvärinen:
Information criteria for non-normalized models. CoRR abs/1905.05976 (2019) - [i6]Nathan Kallus, Masatoshi Uehara:
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning. CoRR abs/1906.03735 (2019) - [i5]Nathan Kallus, Masatoshi Uehara:
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes. CoRR abs/1908.08526 (2019) - [i4]Nathan Kallus, Masatoshi Uehara:
Efficiently Breaking the Curse of Horizon: Double Reinforcement Learning in Infinite-Horizon Processes. CoRR abs/1909.05850 (2019) - [i3]Masatoshi Uehara, Nan Jiang:
Minimax Weight and Q-Function Learning for Off-Policy Evaluation. CoRR abs/1910.12809 (2019) - [i2]Nathan Kallus, Xiaojie Mao, Masatoshi Uehara:
Localized Debiased Machine Learning: Efficient Estimation of Quantile Treatment Effects, Conditional Value at Risk, and Beyond. CoRR abs/1912.12945 (2019) - 2018
- [i1]Masatoshi Uehara, Takeru Matsuda, Fumiyasu Komaki:
Analysis of Noise Contrastive Estimation from the Perspective of Asymptotic Variance. CoRR abs/1808.07983 (2018)
Coauthor Index
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last updated on 2024-09-25 00:43 CEST by the dblp team
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