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Honda, Junya

Graduate School of Informatics, Department of Informatics Associate Professor

Honda, Junya
list
    Last Updated :2025/05/27

    Basic Information

    Faculty

    • 工学部

    Academic Degree

    • 修士(情報理工学)(東京大学)
    • 博士(科学)(東京大学)

    Academic Resume (Graduate Schools)

    • 東京大学, 大学院情報理工学系研究科数理情報学専攻修士課程, 修了
    • 東京大学, 大学院新領域創成科学研究科複雑理工学専攻博士課程, 修了

    Academic Resume (Undergraduate School/Majors)

    • 東京大学, 工学部計数工学科, 卒業

    Research History

    • From Jan. 2021, To Present
      Kyoto University, Department of Systems Science, Graduate School of Informatics, Associate Professor
    • From Jan. 2017, To Dec. 2020
      The University of Tokyo, Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, Lecturer
    • From Mar. 2013, To Dec. 2016
      The University of Tokyo, Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, Research Associate

    ID,URL

    researchmap URL

    list
      Last Updated :2025/05/27

      Research

      Research Topics, Overview of the research

      • Research Topics

        Machine learning, bandit problems, reinforcement learning.
      • Overview of the research

        We are researching online decision making problems in machine learning such as bandit problems and reinforcement learning. For these topics we are studying construction of good algorithms and their limitations from theoretical and practical viewpoints.

      Papers

      • Active model selection: A variance minimization approach
        Satoshi Hara; Mitsuru Matsuura; Junya Honda; Shinji Ito
        Machine Learning, 21 Nov. 2024
      • Learning with Posterior Sampling for Revenue Management under Time-varying Demand.
        Kazuma Shimizu; Junya Honda; Shinji Ito; Shinji Nakadai
        Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence(IJCAI), 2024
      • Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring.
        Taira Tsuchiya; Shinji Ito; Junya Honda
        Forty-first International Conference on Machine Learning(ICML), 2024
      • Spatiotemporal dynamics of odor representations in the human brain revealed by EEG decoding
        Mugihiko Kato; Toshiki Okumura; Yasuhiro Tsubo; Junya Honda; Masashi Sugiyama; Kazushige Touhara; Masako Okamoto
        Proceedings of the National Academy of Sciences, 24 May 2022, Peer-reviewed
      • Follow-the-Perturbed-Leader with Fréchet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds.
        Jongyeong Lee; Junya Honda; Shinji Ito; Min-hwan Oh
        COLT, 2024
      • Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds.
        Shinji Ito; Taira Tsuchiya; Junya Honda
        COLT, 2024
      • Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds.
        Taira Tsuchiya; Shinji Ito; Junya Honda
        NeurIPS, 2023
      • Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits.
        Jongyeong Lee; Junya Honda; Chao-Kai Chiang; Masashi Sugiyama
        ICML, 2023
      • Thompson Exploration with Best Challenger Rule in Best Arm Identification.
        Jongyeong Lee; Junya Honda; Masashi Sugiyama
        ACML, 2023
      • Further Adaptive Best-of-Both-Worlds Algorithm for Combinatorial Semi-Bandits.
        Taira Tsuchiya; Shinji Ito; Junya Honda
        AISTATS, 2023
      • Best-of-Both-Worlds Algorithms for Partial Monitoring.
        Taira Tsuchiya; Shinji Ito; Junya Honda
        ALT, 2023, Peer-reviewed
      • Follow-the-Perturbed-Leader Achieves Best-of-Both-Worlds for Bandit Problems.
        Junya Honda; Shinji Ito; Taira Tsuchiya
        ALT, 2023, Peer-reviewed
      • Bayesian optimization with partially specified queries.
        Shogo Hayashi; Junya Honda; Hisashi Kashima
        Machine Learning, 2022, Peer-reviewed
      • Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification.
        Junpei Komiyama; Taira Tsuchiya; Junya Honda
        NeurIPS, 2022, Peer-reviewed
      • Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs.
        Shinji Ito; Taira Tsuchiya; Junya Honda
        NeurIPS, 2022, Peer-reviewed
      • Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds.
        Shinji Ito; Taira Tsuchiya; Junya Honda
        COLT, 2022, Peer-reviewed
      • Bandit Algorithms Based on Thompson Sampling for Bounded Reward Distributions.
        Charles Riou; Junya Honda
        Algorithmic Learning Theory(ALT), 2020, Peer-reviewed
      • Optimal dose escalation methods using deep reinforcement learning in phase I oncology trials.
        Kentaro Matsuura; Kentaro Sakamaki; Junya Honda; Takashi Sozu
        Journal of biopharmaceutical statistics, 30 Jan. 2023, Peer-reviewed
      • Polynomial-time Algorithms for Combinatorial Pure Exploration with Full-bandit Feedback.
        Yuko Kuroki; Liyuan Xu; Atsushi Miyauchi 0001; Junya Honda; Masashi Sugiyama
        CoRR, 2019
      • Optimal adaptive allocation using deep reinforcement learning in a dose-response study.
        Kentaro Matsuura; Junya Honda; Imad El Hanafi; Takashi Sozu; Kentaro Sakamaki
        Statistics in medicine, 07 Nov. 2021, Peer-reviewed
      • Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences.
        Ikko Yamane; Junya Honda; Florian Yger; Masashi Sugiyama
        Proceedings of the 38th International Conference on Machine Learning(ICML), 2021, Peer-reviewed
      • Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring.
        Taira Tsuchiya; Junya Honda; Masashi Sugiyama
        Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020(NeurIPS), 2020, Peer-reviewed
      • Online Dense Subgraph Discovery via Blurred-Graph Feedback.
        Yuko Kuroki; Atsushi Miyauchi 0001; Junya Honda; Masashi Sugiyama
        Proceedings of the 37th International Conference on Machine Learning(ICML), 2020, Peer-reviewed
      • Polynomial-Time Algorithms for Multiple-Arm Identification with Full-Bandit Feedback.
        Yuko Kuroki; Liyuan Xu; Atsushi Miyauchi 0001; Junya Honda; Masashi Sugiyama
        Neural Comput., 2020, Peer-reviewed
      • A bad arm existence checking problem: How to utilize asymmetric problem structure?
        Tabata, Koji; Nakamura, Atsuyoshi; Honda, Junya; Komatsuzaki, Tamiki
        MACHINE LEARNING, Oct. 2019, Peer-reviewed
      • Good arm identification via bandit feedback
        Hideaki Kano; Junya Honda; Kentaro Sakamaki; Kentaro Matsuura; Atsuyoshi Nakamura; Masashi Sugiyama
        MACHINE LEARNING, May 2019, Peer-reviewed
      • Uncoupled Regression from Pairwise Comparison Data.
        Liyuan Xu; Junya Honda; Gang Niu; Masashi Sugiyama
        Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019(NeurIPS), 2019, Peer-reviewed
      • On the Calibration of Multiclass Classification with Rejection.
        Chenri Ni; Nontawat Charoenphakdee; Junya Honda; Masashi Sugiyama
        Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019(NeurIPS), 2019, Peer-reviewed
      • Learning from Positive and Unlabeled Data with a Selection Bias.
        Masahiro Kato; Takeshi Teshima; Junya Honda
        7th International Conference on Learning Representations, 2019, Peer-reviewed
      • Unsupervised Domain Adaptation Based on Source-Guided Discrepancy
        Seiichi Kuroki; Nontawat Charoenphakdee; Han Bao; Junya Honda; Issei Sato; Masashi Sugiyama
        THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2019, Peer-reviewed
      • Dueling Bandits with Qualitative Feedback
        Liyuan Xu; Junya Honda; Masashi Sugiyama
        THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2019, Peer-reviewed
      • Exploring a potential energy surface by machine learning for characterizing atomic transport
        Kanamori, Kenta; Toyoura, Kazuaki; Honda, Junya; Hattori, Kazuki; Seko, Atsuto; Karasuyama, Masayuki; Shitara, Kazuki; Shiga, Motoki; Kuwabara, Akihide; Takeuchi, Ichiro
        PHYSICAL REVIEW B, Mar. 2018, Peer-reviewed
      • Nonconvex Optimization for Regression with Fairness Constraints.
        Junpei Komiyama; Akiko Takeda; Junya Honda; Hajime Shimao
        Proceedings of the 35th International Conference on Machine Learning(ICML), 2018, Peer-reviewed
      • A fully adaptive algorithm for pure exploration in linear bandits.
        Liyuan Xu; Junya Honda; Masashi Sugiyama
        International Conference on Artificial Intelligence and Statistics(AISTATS), 2018, Peer-reviewed
      • Normal Bandits of Unknown Means and Variances
        Cowan, Wesley; Honda, Junya; Katehakis, Michael N.
        JOURNAL OF MACHINE LEARNING RESEARCH, 2018, Peer-reviewed
      • Exact Asymptotics of Random Coding Error Probability for General Memoryless Channels
        Honda, Junya
        2018 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT), 2018, Peer-reviewed
      • Worst-case Redundancy of Optimal Binary AIFV Codes and Their Extended Codes
        Hu, Weihua; Yamamoto, Hirosuke; Honda, Junya
        IEEE TRANSACTIONS ON INFORMATION THEORY, Aug. 2017, Peer-reviewed
      • Variable-to-Fixed Length Homophonic Coding Suitable for Asymmetric Channel Coding
        Honda, Junya; Yamamoto, Hirosuke
        2017 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT), 2017, Peer-reviewed
      • Position-based Multiple-play Bandit Problem with Unknown Position Bias.
        Junpei Komiyama; Junya Honda; Akiko Takeda
        ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017), 2017, Peer-reviewed
      • Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm.
        Junpei Komiyama; Junya Honda; Hiroshi Nakagawa
        Proceedings of the 33nd International Conference on Machine Learning(ICML), 2016, Peer-reviewed
      • Variable-to-Fixed Length Homophonic Coding with a Modified Shannon-Fano-Flias Code
        Honda, Junya; Yamamoto, Hirosuke
        PROCEEDINGS OF 2016 INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY AND ITS APPLICATIONS (ISITA 2016), 2016, Peer-reviewed
      • Tight Upper Bounds on the Redundancy of Optimal Binary AIFV Codes
        Hu, Weihua; Yamamoto, Hirosuke; Honda, Junya
        2016 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY, 2016, Peer-reviewed
      • Non-Asymptotic Analysis of a New Bandit Algorithm for Semi-Bounded Reward
        Honda, Junya; Takemura, Akimichi
        JOURNAL OF MACHINE LEARNING RESEARCH, Dec. 2015, Peer-reviewed
      • Almost Instantaneous Fixed-to-Variable Length Codes
        Yamamoto, Hirosuke; Tsuchihashi, Masato; Honda, Junya
        IEEE TRANSACTIONS ON INFORMATION THEORY, Dec. 2015, Peer-reviewed
      • Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays.
        Junpei Komiyama; Junya Honda; Hiroshi Nakagawa
        Proceedings of the 32nd International Conference on Machine Learning(ICML), 2015, Peer-reviewed
      • Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem.
        Junpei Komiyama; Junya Honda; Hisashi Kashima; Hiroshi Nakagawa
        Proceedings of The 28th Conference on Learning Theory(COLT), 2015, Peer-reviewed
      • Exact Asymptotics for the Random Coding Error Probability
        Honda, Junya
        2015 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT), 2015, Peer-reviewed
      • Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring
        Komiyama, Junpei; Honda, Junya; Nakagawa, Hiroshi
        ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 28 (NIPS 2015), 2015, Peer-reviewed
      • FV Polar Coding for Lossy Compression with an Improved Exponent
        Wang, Runxin; Honda, Junya; Yamamoto, Hirosuke; Liu, Rongke
        2015 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY (ISIT), 2015, Peer-reviewed
      • Construction of Polar Codes for Channels with Memory
        Wang, Runxin; Honda, Junya; Yamamoto, Hirosuke; Liu, Rongke; Hou, Yi
        2015 IEEE INFORMATION THEORY WORKSHOP - FALL (ITW), 2015, Peer-reviewed
      • Variable Length Lossy Coding Using an LDPC Code
        Honda, Junya; Yamamoto, Hirosuke
        IEEE TRANSACTIONS ON INFORMATION THEORY, Jan. 2014, Peer-reviewed
      • Optimality of Thompson Sampling for Gaussian Bandits Depends on Priors
        Honda, Junya; Takemura, Akimichi
        ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 33, 2014, Peer-reviewed
      • RSA Meets DPA: Recovering RSA Secret Keys from Noisy Analog Data
        Kunihiro, Noboru; Honda, Junya
        CRYPTOGRAPHIC HARDWARE AND EMBEDDED SYSTEMS - CHES 2014, 2014, Peer-reviewed
      • Polar Coding Without Alphabet Extension for Asymmetric Models
        Honda, Junya; Yamamoto, Hirosuke
        IEEE TRANSACTIONS ON INFORMATION THEORY, Dec. 2013, Peer-reviewed
      • Stochastic bandit based on empirical moments
        Junya Honda; Akimichi Takemura
        Journal of Machine Learning Research, 2012, Peer-reviewed
      • Polar Coding without Alphabet Extension for Asymmetric Channels
        Honda, Junya; Yamamoto, Hirosuke
        2012 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY PROCEEDINGS (ISIT), 2012, Peer-reviewed
      • Fast Linear-Programming Decoding of LDPC Codes over GF(2(m))
        Honda, Junya; Yamamoto, Hirosuke
        2012 INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY AND ITS APPLICATIONS (ISITA 2012), 2012, Peer-reviewed
      • An asymptotically optimal policy for finite support models in the multiarmed bandit problem
        Honda, Junya; Takemura, Akimichi
        MACHINE LEARNING, Dec. 2011, Peer-reviewed
      • An asymptotically optimal bandit algorithm for bounded support models
        Junya Honda; Akimichi Takemura
        COLT 2010 - The 23rd Conference on Learning Theory, 2010, Peer-reviewed
      • Variable Length Lossy Coding using an LDPC Code
        Honda, Junya; Yamamoto, Hirosuke
        2009 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY, VOLS 1- 4, 2009, Peer-reviewed
      • Application of LCLP to Lossy Source Coding
        Miyake, Shigeki; Honda, Junya; Yamamoto, Hirosuke
        2008 INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY AND ITS APPLICATIONS, VOLS 1-3, 2008, Peer-reviewed

      Misc.

      • Construction of Almost Instantaneous FV codes based on Integer Programming
        TSUCHIHASHI Masato; YAMAMOTO Hirosuke; HONDA Junya
        IEICE technical report. Information theory, 17 Jul. 2014
      • Variable Length Coding of Asymmetric Channels using a Homophonic Code
        HONDA Junya; YAMAMOTO Hirosuke
        IEICE technical report. Information theory, 12 Jul. 2012
      • On a Non-asymptotic Analysis Using Large Deviation Principles in the Multiarmed Bandit Problem
        HONDA Junya; TAKEMURA Akimichi
        電子情報通信学会技術研究報告. IBISML, 情報論的学習理論と機械学習 = IEICE technical report. IBISML, Information-based induction sciences and machine learning, 12 Jun. 2012
      • Bad Arm Existence Checking Algorithms with Small Sample Complexity
        田畑 公次; 中村 篤祥; 本多 淳也; 小松崎 民樹
        人工知能基本問題研究会, 16 Mar. 2018
      • Source Coding with LDPC Code using Linear Programming
        HONDA Junya; MIYAKE Shigeki; YAMAMOTO Hirosuke; MARUYAMA Mitsuru
        IEICE technical report, 17 Jul. 2008

      Books and Other Publications

      External funds: Kakenhi

      • バンディット問題における最適性達成のためのランダム方策の発展と解析
        Grant-in-Aid for Scientific Research (C)
        Basic Section 60010:Theory of informatics-related
        Kyoto University
        本多 淳也
        From 01 Apr. 2021, To 31 Mar. 2025, Granted
        機械学習;バンディット問題;オンライン学習;治験;学習理論;実験計画;情報理論
      • Construction of knowledge discovery algorithms based on information theoretic methods
        Grant-in-Aid for Early-Career Scientists
        Basic Section 60010:Theory of informatics-related
        Kyoto University;The University of Tokyo
        Junya Honda
        From 01 Apr. 2018, To 31 Mar. 2021, Project Closed
        機械学習;情報理論;バンディット問題
      • 確率モデルに基づいた結晶構造の学習および推定法の確立
        Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)
        Science and Engineering
        The University of Tokyo
        本多 淳也
        From 01 Apr. 2016, To 31 Mar. 2018, Project Closed
        機械学習;実験計画法;材料探索;材料科学;計算物理
      • 効率的な材料探索のための実験計画法の開発
        Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)
        Science and Engineering
        The University of Tokyo
        本多 淳也
        From 01 Apr. 2014, To 31 Mar. 2016, Project Closed
        機械学習
      • 多腕バンディット問題における最適戦略の構成と発展
        Grant-in-Aid for Research Activity Start-up
        The University of Tokyo
        本多 淳也
        From 30 Aug. 2013, To 31 Mar. 2015, Declined
        機械学習;統計学;多腕バンディット問題
      • 多様な環境に同時最適となる頑健な動的意思決定方策の基盤技術
        Grant-in-Aid for Scientific Research (B)
        Basic Section 61030:Intelligent informatics-related
        Kyoto University
        本多 淳也
        From 01 Apr. 2025, To 31 Mar. 2030, Adopted
      list
        Last Updated :2025/05/27

        Education

        Teaching subject(s)

        • From 01 Apr. 2024, To 31 Mar. 2025
          Mathematical theory of information and communications
          9142, Fall, Faculty of Engineering, 2
        • From 01 Apr. 2024, To 31 Mar. 2025
          Introduction to Applied Mathematics and Physics
          9114, Spring, Faculty of Engineering, 2
        • From 01 Apr. 2024, To 31 Mar. 2025
          Stochastic Discrete Event Systems
          9096, Spring, Faculty of Engineering, 2
        • From 01 Apr. 2024, To 31 Mar. 2025
          Systems Science, Advanced II
          3512, Fall, Graduate School of Informatics, 2
        • From 01 Apr. 2024, To 31 Mar. 2025
          Systems Science, Advanced I
          3542, Spring, Graduate School of Informatics, 2
        • From 01 Apr. 2024, To 31 Mar. 2025
          Statistical Systems Theory
          3536, Spring, Graduate School of Informatics, 2
        • From 01 Apr. 2024, To 31 Mar. 2025
          Statistical Systems Theory
          M310, Spring, Institute for Liberal Arts and Sciences, 2
        • From 01 Apr. 2023, To 31 Mar. 2024
          Stochastic Discrete Event Systems
          9096, Spring, Faculty of Engineering, 2
        • From 01 Apr. 2023, To 31 Mar. 2024
          Mathematical theory of information and communications
          9142, Fall, Faculty of Engineering, 2
        • From 01 Apr. 2023, To 31 Mar. 2024
          Systems Science, Advanced I
          3542, Spring, Graduate School of Informatics, 2
        • From 01 Apr. 2023, To 31 Mar. 2024
          Statistical Systems Theory
          3536, Spring, Graduate School of Informatics, 2
        • From 01 Apr. 2023, To 31 Mar. 2024
          Systems Science, Advanced II
          3512, Fall, Graduate School of Informatics, 2
        • From 01 Apr. 2023, To 31 Mar. 2024
          ILAS Seminar
          Z001, Spring, Institute for Liberal Arts and Sciences, 2
        • From 01 Apr. 2023, To 31 Mar. 2024
          Statistical Systems Theory
          M310, Spring, Institute for Liberal Arts and Sciences, 2
        • From 01 Apr. 2022, To 31 Mar. 2023
          Mathematical theory of information and communications
          9142, Fall, Faculty of Engineering, 2
        • From 01 Apr. 2022, To 31 Mar. 2023
          Seminar on Applied Mathematics and Physics
          9074, Fall, Faculty of Engineering, 2
        • From 01 Apr. 2022, To 31 Mar. 2023
          Systems Science, Advanced I
          3542, Spring, Graduate School of Informatics, 2
        • From 01 Apr. 2022, To 31 Mar. 2023
          Systems Science, Advanced II
          3512, Fall, Graduate School of Informatics, 2
        • From 01 Apr. 2022, To 31 Mar. 2023
          Statistical Systems Theory
          3536, Spring, Graduate School of Informatics, 2
        • From 01 Apr. 2022, To 31 Mar. 2023
          Statistical Systems Theory
          M310, Spring, Institute for Liberal Arts and Sciences, 2
        • From 01 Apr. 2022, To 31 Mar. 2023
          Stochastic Discrete Event Systems
          9096, Spring, Faculty of Engineering, 2
        • From Apr. 2021, To Mar. 2022
          Mathematical theory of information and communications
          Fall, 工学部
        • From Apr. 2021, To Mar. 2022
          Stochastic Discrete Event Systems
          Spring, 工学部
        • From Apr. 2021, To Mar. 2022
          Systems Science, Advanced II
          Fall, 情報学研究科
        • From Apr. 2021, To Mar. 2022
          Systems Science, Advanced I
          Spring, 情報学研究科
        • From Apr. 2021, To Mar. 2022
          Statistical Systems Theory
          Spring, 情報学研究科

        Participation in PhD Defense

        • Environment Adaptive Regret Analysis in Bandit Problems
          TSUCHIYA TAIRA, Graduate School of Informatics, Chief Examiner
          25 Sep. 2023
        • Convergence Formulas for the Level-increment Truncation Approximation of M/G/1-type Markov Chains
          OUCHI KATSUHISA, Graduate School of Informatics, Sub-chief Examiner
          24 Nov. 2023
        list
          Last Updated :2025/05/27

          Administration

          Faculty management (title, position)

          • From 01 Apr. 2021, To 31 Mar. 2023
            広報WG委員

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