Multi-armed Bandit Problem with Lock-up Periods. - Junpei Komiyama; Issei Sato; Hiroshi Nakagawa (ACML, 2013)

Robust Distributed Training of Linear Classifiers Based on Divergence Minimization Principle. - Junpei Komiyama; Hidekazu Oiwa; Hiroshi Nakagawa (ECML/PKDD (2), 2014)

Time-Decaying Bandits for Non-stationary Systems. - Junpei Komiyama; Tao Qin (WINE, 2014)

Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem. - Junpei Komiyama; Junya Honda; Hisashi Kashima; Hiroshi Nakagawa (COLT, 2015)

Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays. - Junpei Komiyama; Junya Honda; Hiroshi Nakagawa (ICML, 2015)

Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring. - Junpei Komiyama; Junya Honda; Hiroshi Nakagawa (NIPS, 2015)

Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm. - Junpei Komiyama; Junya Honda; Hiroshi Nakagawa (ICML, 2016)

Road safety estimation utilizing big and heterogeneous vehicle recorder data. - Masashi Toyoda; Daisaku Yokoyama; Junpei Komiyama; Masahiko Itoh (IEEE BigData, 2017)

Statistical Emerging Pattern Mining with Multiple Testing Correction. - Junpei Komiyama; Masakazu Ishihata; Hiroki Arimura; Takashi Nishibayashi; Shin-ichi Minato (KDD, 2017)

Position-based Multiple-play Bandit Problem with Unknown Position Bias. - Junpei Komiyama; Junya Honda; Akiko Takeda (NIPS, 2017)

Two-stage Algorithm for Fairness-aware Machine Learning. - Junpei Komiyama; Hajime Shimao (CoRR, 2017)

KL-UCB-Based Policy for Budgeted Multi-Armed Bandits with Stochastic Action Costs. - Ryo Watanabe; Junpei Komiyama; Atsuyoshi Nakamura; Mineichi Kudo (IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2017)

Nonconvex Optimization for Regression with Fairness Constraints. - Junpei Komiyama; Akiko Takeda; Junya Honda; Hajime Shimao (ICML, 2018)

Comparing Fairness Criteria Based on Social Outcome. - Junpei Komiyama; Hajime Shimao (CoRR, 2018)

A Simple Way to Deal with Cherry-picking. - Junpei Komiyama; Takanori Maehara (CoRR, 2018)

UCB-SC: A Fast Variant of KL-UCB-SC for Budgeted Multi-Armed Bandit Problem. - Ryo Watanabe; Junpei Komiyama; Atsuyoshi Nakamura; Mineichi Kudo (IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2018)

Scaling Multi-Armed Bandit Algorithms. - Edouard Fouché; Junpei Komiyama; Klemens Böhm (KDD, 2019)

A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy. - Kei Nakagawa; Masaya Abe; Junpei Komiyama (CoRR, 2019)

RIC-NN: A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy. - Kei Nakagawa; Masaya Abe; Junpei Komiyama (DSAA, 2020)

On Statistical Discrimination as a Failure of Social Learning: A Multi-Armed Bandit Approach. - Junpei Komiyama; Shunya Noda (CoRR, 2020)

Controlling False Discovery Rates Using Null Bootstrapping. - Junpei Komiyama; Masaya Abe; Kei Nakagawa; Kenichiro McAlinn (CoRR, 2021)

Policy Choice and Best Arm Identification: Comments on "Adaptive Treatment Assignment in Experiments for Policy Choice". - Kaito Ariu; Masahiro Kato; Junpei Komiyama; Kenichiro McAlinn (CoRR, 2021)

Deviation-Based Learning. - Junpei Komiyama; Shunya Noda (CoRR, 2021)

Optimal Simple Regret in Bayesian Best Arm Identification. - Junpei Komiyama; Kaito Ariu; Masahiro Kato; Chao Qin (CoRR, 2021)

Anytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search. - Kenshi Abe; Junpei Komiyama; Atsushi Iwasaki (IJCAI, 2022)

Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification. - Junpei Komiyama; Taira Tsuchiya; Junya Honda (NeurIPS, 2022)

Bayes Optimal Algorithm is Suboptimal in Frequentist Best Arm Identification. - Junpei Komiyama (CoRR, 2022)

Globally Optimal Algorithms for Fixed-Budget Best Arm Identification. - Junpei Komiyama; Taira Tsuchiya; Junya Honda (CoRR, 2022)

Strategic Choices of Migrants and Smugglers in the Central Mediterranean Sea. - Katherine Hoffmann Pham; Junpei Komiyama (CoRR, 2022)

Bridging Offline and Online Experimentation: Constraint Active Search for Deployed Performance Optimization. - Junpei Komiyama; Gustavo Malkomes; Bolong Cheng; Michael McCourt (Trans. Mach. Learn. Res., 2022)

Thresholded linear bandits. - Nishant A. Mehta; Junpei Komiyama; Vamsi K. Potluru; Andrea Nguyen; Mica Grant-Hagen (AISTATS, 2023)

Posterior Tracking Algorithm for Classification Bandits. - Koji Tabata; Junpei Komiyama; Atsuyoshi Nakamura; Tamiki Komatsuzaki (AISTATS, 2023)

High-dimensional Contextual Bandit Problem without Sparsity. - Junpei Komiyama; Masaaki Imaizumi (NeurIPS, 2023)

Learning Fair Division from Bandit Feedback. - Hakuei Yamada; Junpei Komiyama; Kenshi Abe; Atsushi Iwasaki (AISTATS, 2024)

Fixed Confidence Best Arm Identification in the Bayesian Setting. - Kyoungseok Jang; Junpei Komiyama; Kazutoshi Yamazaki (NeurIPS, 2024)

Replicability is Asymptotically Free in Multi-armed Bandits. - Junpei Komiyama; Shinji Ito; Yuichi Yoshida; Souta Koshino (CoRR, 2024)

Finite-time Analysis of Globally Nonstationary Multi-Armed Bandits. - Junpei Komiyama; Edouard Fouché; Junya Honda (J. Mach. Learn. Res., 2024)

Rate-Optimal Bayesian Simple Regret in Best Arm Identification. - Junpei Komiyama; Kaito Ariu; Masahiro Kato; Chao Qin (Math. Oper. Res., 2024)

Data-dependent Bounds with T-Optimal Best-of-Both-Worlds Guarantees in Multi-Armed Bandits using Stability-Penalty Matching. - Quan Nguyen; Shinji Ito; Junpei Komiyama; Nishant Mehta (COLT, 2025)

No-Regret Incentive-Compatible Online Learning under Exact Truthfulness with Non-Myopic Experts. - Junpei Komiyama; Nishant A. Mehta; Ali Mortazavi (EC, 2025)

Data-dependent Bounds with T-Optimal Best-of-Both-Worlds Guarantees in Multi-Armed Bandits using Stability-Penalty Matching. - Quan Nguyen; Shinji Ito; Junpei Komiyama; Nishant A. Mehta (CoRR, 2025)

High-dimensional Nonparametric Contextual Bandit Problem. - Shogo Iwazaki; Junpei Komiyama; Masaaki Imaizumi (CoRR, 2025)