| Optimal regret analysis of thompson sampling in stochastic multi-armed bandit problem with multiple plays J Komiyama, J Honda, H Nakagawa International Conference on Machine Learning, 1152-1161, 2015 | 178 | 2015 |
| Nonconvex optimization for regression with fairness constraints J Komiyama, A Takeda, J Honda, H Shimao International conference on machine learning, 2737-2746, 2018 | 113 | 2018 |
| Regret lower bound and optimal algorithm in dueling bandit problem J Komiyama, J Honda, H Kashima, H Nakagawa Conference on learning theory, 1141-1154, 2015 | 81 | 2015 |
| Copeland dueling bandit problem: Regret lower bound, optimal algorithm, and computationally efficient algorithm J Komiyama, J Honda, H Nakagawa International Conference on Machine Learning, 1235-1244, 2016 | 38 | 2016 |
| Scaling multi-armed bandit algorithms E Fouché, J Komiyama, K Böhm Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 30 | 2019 |
| Regret lower bound and optimal algorithm in finite stochastic partial monitoring J Komiyama, J Honda, H Nakagawa Advances in Neural Information Processing Systems 28, 2015 | 29 | 2015 |
| Statistical emerging pattern mining with multiple testing correction J Komiyama, M Ishihata, H Arimura, T Nishibayashi, S Minato Proceedings of the 23rd ACM SIGKDD international conference on knowledge …, 2017 | 28 | 2017 |
| Position-based multiple-play bandit problem with unknown position bias J Komiyama, J Honda, A Takeda Advances in Neural Information Processing Systems 30, 2017 | 28 | 2017 |
| Two-stage algorithm for fairness-aware machine learning J Komiyama, H Shimao arXiv preprint arXiv:1710.04924, 2017 | 21 | 2017 |
| Ric-nn: a robust transferable deep learning framework for cross-sectional investment strategy K Nakagawa, M Abe, J Komiyama 2020 IEEE 7th International Conference on Data Science and Advanced …, 2020 | 18 | 2020 |
| Multi-armed bandit problem with lock-up periods J Komiyama, I Sato, H Nakagawa Asian Conference on Machine Learning, 100-115, 2013 | 17 | 2013 |
| Time-decaying bandits for non-stationary systems J Komiyama, T Qin Web and Internet Economics: 10th International Conference, WINE 2014 …, 2014 | 15 | 2014 |
| Minimax Optimal Algorithms for Fixed-Budged Best Arm Identification J Komiyama, T Tsuchiya, J Honda arXiv preprint arXiv:2206.04646, 2022 | 11 | 2022 |
| Kl-ucb-based policy for budgeted multi-armed bandits with stochastic action costs R Watanabe, J Komiyama, A Nakamura, M Kudo IEICE Transactions on Fundamentals of Electronics, Communications and …, 2017 | 11 | 2017 |
| Optimal simple regret in Bayesian best arm identification J Komiyama, K Ariu, M Kato, C Qin arXiv preprint arXiv:2111.09885, 2021 | 8 | 2021 |
| Policy choice and best arm identification: Asymptotic analysis of exploration sampling K Ariu, M Kato, J Komiyama, K McAlinn, C Qin arXiv preprint arXiv:2109.08229, 2021 | 8 | 2021 |
| Anytime capacity expansion in medical residency match by monte carlo tree search K Abe, J Komiyama, A Iwasaki arXiv preprint arXiv:2202.06570, 2022 | 6 | 2022 |
| On statistical discrimination as a failure of social learning: A multi-armed bandit approach J Komiyama, S Noda arXiv preprint arXiv:2010.01079, 2020 | 4 | 2020 |
| A simple way to deal with cherry-picking J Komiyama, T Maehara arXiv preprint arXiv:1810.04996, 2018 | 4 | 2018 |
| Comparing fairness criteria based on social outcome J Komiyama, H Shimao arXiv preprint arXiv:1806.05112, 2018 | 4 | 2018 |
Hisashi KashimaProfessor, Kyoto University確認したメール アドレス: i.kyoto-u.ac.jp
