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Junpei Komiyama
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2020 – today
- 2024
- [j5]Junpei Komiyama, Edouard Fouché, Junya Honda:
Finite-time Analysis of Globally Nonstationary Multi-Armed Bandits. J. Mach. Learn. Res. 25: 112:1-112:56 (2024) - [j4]Junpei Komiyama
, Kaito Ariu
Rate-Optimal Bayesian Simple Regret in Best Arm Identification. Math. Oper. Res. 49(3): 1629-1646 (2024) - [c19]Hakuei Yamada, Junpei Komiyama, Kenshi Abe, Atsushi Iwasaki:
Learning Fair Division from Bandit Feedback. AISTATS 2024: 3106-3114 - [i22]Junpei Komiyama, Shinji Ito, Yuichi Yoshida, Souta Koshino:
Replicability is Asymptotically Free in Multi-armed Bandits. CoRR abs/2402.07391 (2024) - [i21]Kyoungseok Jang
Fixed Confidence Best Arm Identification in the Bayesian Setting. CoRR abs/2402.10429 (2024) - 2023
- [c18]Nishant A. Mehta, Junpei Komiyama, Vamsi K. Potluru, Andrea Nguyen, Mica Grant-Hagen:
Thresholded linear bandits. AISTATS 2023: 6968-7020 - [c17]Koji Tabata, Junpei Komiyama, Atsuyoshi Nakamura, Tamiki Komatsuzaki:
Posterior Tracking Algorithm for Classification Bandits. AISTATS 2023: 10994-11022 - [c16]Junpei Komiyama, Masaaki Imaizumi:
High-dimensional Contextual Bandit Problem without Sparsity. NeurIPS 2023 - [i20]Junpei Komiyama, Masaaki Imaizumi:
High-dimensional Contextual Bandit Problem without Sparsity. CoRR abs/2306.11017 (2023) - [i19]Hakuei Yamada, Junpei Komiyama, Kenshi Abe, Atsushi Iwasaki:
Learning Fair Division from Bandit Feedback. CoRR abs/2311.09068 (2023) - 2022
- [j3]Junpei Komiyama, Gustavo Malkomes, Bolong Cheng, Michael McCourt:
Bridging Offline and Online Experimentation: Constraint Active Search for Deployed Performance Optimization. Trans. Mach. Learn. Res. 2022 (2022) - [c15]Kenshi Abe, Junpei Komiyama, Atsushi Iwasaki:
Anytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search. IJCAI 2022: 3-9 - [c14]Junpei Komiyama, Taira Tsuchiya, Junya Honda:
Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification. NeurIPS 2022 - [i18]Junpei Komiyama:
Bayes Optimal Algorithm is Suboptimal in Frequentist Best Arm Identification. CoRR abs/2202.05193 (2022) - [i17]Kenshi Abe, Junpei Komiyama, Atsushi Iwasaki:
Anytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search. CoRR abs/2202.06570 (2022) - [i16]Junpei Komiyama, Taira Tsuchiya, Junya Honda:
Globally Optimal Algorithms for Fixed-Budget Best Arm Identification. CoRR abs/2206.04646 (2022) - [i15]Katherine Hoffmann Pham, Junpei Komiyama:
Strategic Choices of Migrants and Smugglers in the Central Mediterranean Sea. CoRR abs/2207.04480 (2022) - 2021
- [i14]Junpei Komiyama, Masaya Abe, Kei Nakagawa, Kenichiro McAlinn:
Controlling False Discovery Rates Using Null Bootstrapping. CoRR abs/2102.07826 (2021) - [i13]Junpei Komiyama, Edouard Fouché, Junya Honda:
Finite-time Analysis of Globally Nonstationary Multi-Armed Bandits. CoRR abs/2107.11419 (2021) - [i12]Kaito Ariu, Masahiro Kato, Junpei Komiyama, Kenichiro McAlinn:
Policy Choice and Best Arm Identification: Comments on "Adaptive Treatment Assignment in Experiments for Policy Choice". CoRR abs/2109.08229 (2021) - [i11]Junpei Komiyama, Shunya Noda:
Deviation-Based Learning. CoRR abs/2109.09816 (2021) - [i10]Junpei Komiyama, Kaito Ariu, Masahiro Kato, Chao Qin:
Optimal Simple Regret in Bayesian Best Arm Identification. CoRR abs/2111.09885 (2021) - 2020
- [c13]Kei Nakagawa
RIC-NN: A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy. DSAA 2020: 370-379 - [i9]Junpei Komiyama, Shunya Noda:
On Statistical Discrimination as a Failure of Social Learning: A Multi-Armed Bandit Approach. CoRR abs/2010.01079 (2020)
2010 – 2019
- 2019
- [c12]Edouard Fouché, Junpei Komiyama, Klemens Böhm:
Scaling Multi-Armed Bandit Algorithms. KDD 2019: 1449-1459 - [i8]Kei Nakagawa
A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy. CoRR abs/1910.01491 (2019) - 2018
- [j2]Ryo Watanabe, Junpei Komiyama, Atsuyoshi Nakamura
UCB-SC: A Fast Variant of KL-UCB-SC for Budgeted Multi-Armed Bandit Problem. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 101-A(3): 662-667 (2018) - [c11]Junpei Komiyama, Akiko Takeda, Junya Honda, Hajime Shimao:
Nonconvex Optimization for Regression with Fairness Constraints. ICML 2018: 2742-2751 - [i7]Junpei Komiyama, Hajime Shimao:
Comparing Fairness Criteria Based on Social Outcome. CoRR abs/1806.05112 (2018) - [i6]Junpei Komiyama, Takanori Maehara:
A Simple Way to Deal with Cherry-picking. CoRR abs/1810.04996 (2018) - 2017
- [j1]Ryo Watanabe, Junpei Komiyama, Atsuyoshi Nakamura
KL-UCB-Based Policy for Budgeted Multi-Armed Bandits with Stochastic Action Costs. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 100-A(11): 2470-2486 (2017) - [c10]Masashi Toyoda, Daisaku Yokoyama, Junpei Komiyama, Masahiko Itoh:
Road safety estimation utilizing big and heterogeneous vehicle recorder data. IEEE BigData 2017: 4841-4842 - [c9]Junpei Komiyama, Masakazu Ishihata, Hiroki Arimura, Takashi Nishibayashi, Shin-ichi Minato:
Statistical Emerging Pattern Mining with Multiple Testing Correction. KDD 2017: 897-906 - [c8]Junpei Komiyama, Junya Honda, Akiko Takeda:
Position-based Multiple-play Bandit Problem with Unknown Position Bias. NIPS 2017: 4998-5008 - [i5]Junpei Komiyama, Hajime Shimao:
Two-stage Algorithm for Fairness-aware Machine Learning. CoRR abs/1710.04924 (2017) - 2016
- [c7]Junpei Komiyama, Junya Honda, Hiroshi Nakagawa:
Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm. ICML 2016: 1235-1244 - [i4]Junpei Komiyama, Junya Honda, Hiroshi Nakagawa:
Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm. CoRR abs/1605.01677 (2016) - 2015
- [c6]Junpei Komiyama, Junya Honda, Hisashi Kashima, Hiroshi Nakagawa:
Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem. COLT 2015: 1141-1154 - [c5]Junpei Komiyama, Junya Honda, Hiroshi Nakagawa:
Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays. ICML 2015: 1152-1161 - [c4]Junpei Komiyama, Junya Honda, Hiroshi Nakagawa:
Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring. NIPS 2015: 1792-1800 - [i3]Junpei Komiyama, Junya Honda, Hiroshi Nakagawa:
Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays. CoRR abs/1506.00779 (2015) - [i2]Junpei Komiyama, Junya Honda, Hisashi Kashima, Hiroshi Nakagawa:
Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem. CoRR abs/1506.02550 (2015) - [i1]Junpei Komiyama, Junya Honda, Hiroshi Nakagawa:
Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring. CoRR abs/1509.09011 (2015) - 2014
- [c3]Junpei Komiyama, Hidekazu Oiwa, Hiroshi Nakagawa:
Robust Distributed Training of Linear Classifiers Based on Divergence Minimization Principle. ECML/PKDD (2) 2014: 1-17 - [c2]Junpei Komiyama, Tao Qin:
Time-Decaying Bandits for Non-stationary Systems. WINE 2014: 460-466 - 2013
- [c1]Junpei Komiyama, Issei Sato, Hiroshi Nakagawa:
Multi-armed Bandit Problem with Lock-up Periods. ACML 2013: 116-132
Coauthor Index
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last updated on 2024-10-07 22:05 CEST by the dblp team
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