Kyohei Okumura 个人学术档案

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Shunya NodaFaculty of Economics, The University of Tokyo在 e.u-tokyo.ac.jp 的电子邮件经过验证
Masahiro KatoMizuho-DL Financial Technology Co., Ltd. / The University of Tokyo在 g.ecc.u-tokyo.ac.jp 的电子邮件经过验证
Narita YusukeYale University在 yale.edu 的电子邮件经过验证
Atsushi IwasakiUniversity of Electro-Communications在 is.uec.ac.jp 的电子邮件经过验证
Yoji TomitaCyberAgent, Inc.在 cyberagent.co.jp 的电子邮件经过验证
Toru KitagawaDepartment of Economics, Brown University在 brown.edu 的电子邮件经过验证
Max Tabord-MeehanUniversity of Chicago在 uchicago.edu 的电子邮件经过验证
Annie LiangNorthwestern University在 northwestern.edu 的电子邮件经过验证
Eric AuerbachAssistant Professor of Economics, Northwestern University在 northwestern.edu 的电子邮件经过验证
Kohei YataUniversity of Wisconsin–Madison在 wisc.edu 的电子邮件经过验证

Kyohei Okumura

Department of Economics, Northwestern University

在 u.northwestern.edu 的电子邮件经过验证 - 首页


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
An economic analysis of difficulty adjustment algorithms in proof-of-work blockchain systems S Noda, K Okumura, Y Hashimoto Available at SSRN 3410460, 2019	34*	2019
A simple, fast, and safe mediator for congestion management K Ikegami, K Okumura, T Yoshikawa Proceedings of the AAAI conference on artificial intelligence 34 (02), 2030-2037, 2020	5	2020
Testing the Fairness-Improvability of Algorithms E Auerbach, A Liang, M Tabord-Meehan, K Okumura arXiv preprint arXiv:2405.04816, 2024	1	2024
Adaptive Experimental Design for Policy Learning M Kato, K Okumura, T Ishihara, T Kitagawa arXiv preprint arXiv:2401.03756, 2024	1	2024
Regulating Matching Markets with Constraints: Data-driven Taxation A Matsushita, K Ikegami, K Okumura, Y Tomita, A Iwasaki arXiv preprint arXiv:2205.14387, 2022	1	2022
Counterfactual learning with general data-generating policies Y Narita, K Okumura, A Shimizu, K Yata Proceedings of the AAAI Conference on Artificial Intelligence 37 (8), 9286-9293, 2023		2023
Off-policy Evaluation with General Logging Policies: Implementation at Mercari Y Narita, K Okumura, A Shimizu, K Yata Research Institute of Economy, Trade and Industry (RIETI), 2022		2022
Adaptive Experimental Design for Policy Learning: Contextual Best Arm Identification M Kato, K Okumura, T Ishihara, T Kitagawa ICML 2024 Workshop: Foundations of Reinforcement Learning and Control …, 0

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