Masatoshi Uehara

Biography

I am an incoming tenure-track assistant professor in the computer science department at the University of Wisconsin-Madison (joining 25 Fall). I received a Ph.D. in the computer science department at Cornell University (23 Aug). Prior to this, I was in the Ph.D. program in the statistics department at Harvard University (I moved after getting a Master's degree). My research interests revolve around the development of foundational machine learning algorithms for sequential decision making.

You can find my Google Scholar profile here. Additionally, my CV is here. I am from Japan, and my name in 漢字 is 上原雅俊.

My research focuses primarily on reinforcement learning, causal machine learning, and online learning. My general focus is how to incorporate flexible function approximation (such as deep neural networks) into these fields in a sample-efficient manner. Currently, I am engaged in research that applies ML techniques to the field of medicine.

My recent works are summarized as follows:

Sample-Efficient Offline Policy Evaluation: Doubly robust and semiparametrically efficient estimators (1,2), Incorporating deep neural networks with minimax loss (1,2). Slide is here.
Robust Offline RL under Insufficient Coverage: Model-based pessimistic offline RL. Talk is here.
Representation Learning in Online RL: Model-based RL in low-rank MDPs. Talk is here.
Causal Inference and Reinforcement Learning: Accounting for Unmeasured Confounders ([1], [2]), Data combination
Integration of Machine Learning Methods into Inverse Problems: Semiparametric IV methods to estimate functionals without identification (+how to perform inference), Nonparametric IV methods without identification Slide is here
Sample-Efficient Reinforcement Learning in Partially Observable MDPs: OPE with general function approximation, Online RL with general function approximation, Computationally and statistically efficient PAC RL methods. Slide is here.

My advisor during the Ph.D. program is Nathan Kallus (Cornell). My committee members are Wen Sun (Cornell), Thorsten Joachims (Cornell), Victor Chernozhukov (MIT), Xiao-Li Meng (Harvard), and Nan Jiang (UIUC).

Publication

Red means I am the co-first/corresponding author. Blue means alphabetical order following the convention. The other papers follow the contribution-based ordering.

Journal Articles

Nathan Kallus and Masatoshi Uehara. Efficient evaluation of natural stochastic policies in offline reinforcement learning. Biometrika, 2023+

Nathan Kallus and Masatoshi Uehara. Efficiently breaking the curse of horizon: Double reinforcement learning in infinite-horizon processes. Operations research, 2021.

Takeru Matsuda, Masatoshi Uehara, and Aapo Hyvarinen. Information criteria for non-normalized models. Journal of Machine Learning Research, 2021.

Nathan Kallus and Masatoshi Uehara. Double reinforcement learning for efficient off-policy evaluation in markov decision processes. Journal of Machine Learning Research, 2020. (Code)

Conference Proceedings

Runzhe Wu, Uehara, Masatoshi, and Wen Sun. Distributional offline policy evaluation with predictive error guarantees. ICML, 2023. (Code)

Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun. Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. arXiv preprint arXiv:2206.12081 ICML 2023

Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, and Masatoshi Uehara. Minimax Instrumental Variable Regression and L2 Convergence Guarantees without Identification or Closedness. arXiv preprint arXiv:2302.05404 COLT 2023

Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, and Masatoshi Uehara. Inference on strongly identified functionals of weakly identified functions. arXiv preprint arXiv:2208 COLT 2023

Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee. PAC Reinforcement Learning for Predictive State Representations ICLR 2023

Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun. Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. Neurips 2022 + arXiv preprint arXiv:2206.12020

Chengchun Shi, Masatoshi Uehara, Jiawei Huang, and Nan Jiang. A minimax learning approach to off-policy evaluation in partially observable markov decision processes. ICML(Long presentation), 2022. (Slide Code)

Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Wen Sun, and Alekh Agarwal. Efficient reinforcement learning in block mdps: A model-free representation learning approach. ICML 2022. Presented at RL THEORY VIRTUAL SEMINAR 2021 by Xuezhou. (Code)

Masatoshi Uehara, Xuezhou Zhang, and Wen Sun. Representation learning for online and offline rl in low-rank mdps. ICLR (Spotlight), 2022. Oral Paper in Ecological Theory of Reinforcement Learning Workshop at Neurips. (Talk Slide)

Masatoshi Uehara and Wen Sun. Pessimistic model-based offline rl: Pac bounds and posterior sampling under partial coverage. ICLR, 2022. Presented at RL THEORY VIRTUAL SEMINAR 2021. (Talk Slide )

Jonathan D Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, and Wen Sun. Mitigating covariate shift in imitation learning via offline data without great coverage. Neurips, 2021. (Code)

Nathan Kallus, Yuta Saito, and Masatoshi Uehara. Optimal off-policy evaluation from multiple logging policies. ICML, 2021. (Code)

Yichun Hu, Nathan Kallus, and Masatoshi Uehara. Fast rates for the regret of offline reinforcement learning. COLT, 2021. Presented at RL THEORY VIRTUAL SEMINAR 2021/11/26 by Yichucn. (“Minor Revision” requested from Mathematics of Operations Research)

Masatoshi Uehara, Masahiro Kato, and Shota Yasui. Off-policy evaluation and learning for external validity under a covariate shift. NeurIPS (Spotlight), 2020. (Talk Code)

Nathan Kallus and Masatoshi Uehara. Doubly robust off-policy value and gradient estimation for deterministic policies. NeurIPS, 2020. (Talk )

Masatoshi Uehara, Jiawei Huang, and Nan Jiang. Minimax weight and q-function learning for off-policy evaluation. ICML, 2020. (Code)

Nathan Kallus and Masatoshi Uehara. Statistically efficient off-policy policy gradients. ICML, 2020.

Nathan Kallus and Masatoshi Uehara. Double reinforcement learning for efficient and robust off-policy evaluation ICML, 2020. (Code )

Masatoshi Uehara, Takeru Matsuda, and Jae Kwang Kim. Imputation estimators for unnormalized models with missing data. AISTATS, 2020.

Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi, and Takeru Matsuda. Unified estimation framework for unnormalized models with statistical efficiency. AISTATS, 2020.

Nathan Kallus and Masatoshi Uehara. Intrinsically efficient, stable, and bounded off-policy evaluation for reinforcement learning. NeurIPS, 2019.

Unpublished Articles Under Revision

Masatoshi Uehara , Chengchun Shi, and Nathan Kallus. An overview of off-policy evaluation in reinforcement learning. arXiv preprint arXiv:2212.06355 (“Minor Revision” requested from Statistical Science )

Masatoshi Uehara, Masaaki Imaizumi, Nan Jiang, Nathan Kallus, Wen Sun, and Tengyang Xie. Finite sample analysis of minimax offline reinforcement learning: Completeness, fast rates and first-order efficiency. arXiv preprint arXiv:2102.02981, 2021. (Rejection with Resubmission from Annals of Statistics) (SLIDE )

Working Drafts

Bennett, Andrew, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, and Masatoshi Uehara. Source Condition Double Robust Inference on Functionals of Inverse Problems. arXiv preprint arXiv:2307.13793 (2023)

Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun. Refined Value-Based Offline RL under Realizability and Partial Coverage. arXiv preprint arXiv:2302.02392.

Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang, Nathan Kallus, Chengchun Shi, and Wen Sun. Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. arXiv preprint arXiv:2207.13081 (SLIDE)

Nathan Kallus, Xiaojie Mao, and Masatoshi Uehara. Causal inference under unmeasured confounding with negative controls: A minimax learning approach. arXiv preprint arXiv:2103.14029, 2021.

Nathan Kallus, Xiaojie Mao, and Masaotshi Uehara. Localized debiased machine learning: Efficient estimation of quantile treatment effects, conditional value at risk, and beyond. arXiv preprint arXiv:1912.12945, 2020. Presented at Online Causal Inference Seminar 2020/9/15.

Masatoshi Uehara and Jae Kwang Kim. Semiparametric response model with nonignorable nonresponse. arXiv preprint arXiv:1810.12519, 2018.

Masatoshi Uehara, Issei Sato, Masahiro Suzuki, Kotaro Nakayama, and Yutaka Matsuo. Generative adversarial nets from a density ratio estimation perspective. arXiv preprint arXiv:1610.02920, 2016.

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