Masatoshi Uehara
- Statistics and Probability top 10%
- Artificial Intelligence
- Management Science and Operations Research
- Computer Networks and Communications
- Management Information Systems
- Co-authors
- Nathan KallusJonathan ChangXiaojie MaoYusuke NaritaRahul KidambiJae Kwang KimNobuyuki ShimizuTakashi Takenouchi
- Topics
- Reinforcement Learning in Robotics (7 papers)Advanced Causal Inference Techniques (4 papers)Statistical Methods and Inference (4 papers)
- Cited by
- Statistics and ProbabilityManagement Science and Operations ResearchManagement Information Systems
- Partner nations
- United StatesJapan
In The Last Decade
Masatoshi Uehara
12 papers receiving 51 citations
Peers
Comparison fields: 5 of 29
- Statistics and Probability 26
- Artificial Intelligence 18
- Management Science and Operations Research 16
- Computer Networks and Communications 9
- Management Information Systems 6
Countries citing papers authored by Masatoshi Uehara
This map shows the geographic impact of Masatoshi Uehara's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Masatoshi Uehara with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Masatoshi Uehara more than expected).
Fields of papers citing papers by Masatoshi Uehara
This network shows the impact of papers produced by Masatoshi Uehara. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Masatoshi Uehara. The network helps show where Masatoshi Uehara may publish in the future.
Co-authorship network of co-authors of Masatoshi Uehara
This figure shows the co-authorship network connecting the top 25 collaborators of Masatoshi Uehara. A scholar is included among the top collaborators of Masatoshi Uehara based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Masatoshi Uehara. Masatoshi Uehara is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 3 | |
| 3 | 5 | |
| 4 | 0 | |
| 5 | 19 | |
| 6 | Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage | 3 |
| 7 | Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies | 1 |
| 8 | A Unified Statistically Efficient Estimation Framework for Unnormalized Models | 2 |
| 9 | Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes | 5 |
| 10 | Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation | 1 |
| 11 | Efficiently Breaking the Curse of Horizon: Double Reinforcement Learning in Infinite-Horizon Processes. | 7 |
| 12 | Localized Debiased Machine Learning: Efficient Estimation of Quantile Treatment Effects, Conditional Value at Risk, and Beyond. | 3 |
| 13 | Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning | 3 |
| 14 | Imputation estimators for unnormalized models with missing data. | 1 |
About Masatoshi Uehara
Masatoshi Uehara is a scholar working on Statistics and Probability, Artificial Intelligence and Management Science and Operations Research, having authored 14 papers that have together received 53 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (7 papers), Advanced Causal Inference Techniques (4 papers) and Statistical Methods and Inference (4 papers). The work is most often cited by research in Statistics and Probability (26 citations), Management Science and Operations Research (16 citations) and Management Information Systems (6 citations). Masatoshi Uehara has collaborated with scholars based in United States and Japan. Frequent co-authors include Nathan Kallus, Jonathan Chang, Xiaojie Mao, Yusuke Narita, Rahul Kidambi, Jae Kwang Kim, Nobuyuki Shimizu, Takashi Takenouchi, Takafumi Kanamori and Yuta Saito. Their work appears in journals such as Biometrika, Operations Research and Mathematics of Operations Research.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.