| 2025 | COLT | Data-dependent Bounds with T-Optimal Best-of-Both-Worlds Guarantees in Multi-Armed Bandits using Stability-Penalty Matching. | Quan M. Nguyen, Shinji Ito, Junpei Komiyama, Nishant A. Mehta |
| 2024 | AISTATS | Learning Fair Division from Bandit Feedback. | Hakuei Yamada, Junpei Komiyama, Kenshi Abe, Atsushi Iwasaki |
| 2023 | AISTATS | Thresholded linear bandits. | Nishant A. Mehta, Junpei Komiyama, Vamsi K. Potluru, Andrea Nguyen, Mica Grant-Hagen |
| 2023 | AISTATS | Posterior Tracking Algorithm for Classification Bandits. | Koji Tabata, Junpei Komiyama, Atsuyoshi Nakamura, Tamiki Komatsuzaki |
| 2022 | IJCAI | Anytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search. | Kenshi Abe, Junpei Komiyama, Atsushi Iwasaki |
| 2020 | DSAA | RIC-NN: A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy. | Kei Nakagawa, Masaya Abe, Junpei Komiyama |
| 2019 | KDD | Scaling Multi-Armed Bandit Algorithms. | Edouard Fouch, Junpei Komiyama, Klemens Bhm |
| 2018 | ICML | Nonconvex Optimization for Regression with Fairness Constraints. | Junpei Komiyama, Akiko Takeda, Junya Honda, Hajime Shimao |
| 2017 | KDD | Statistical Emerging Pattern Mining with Multiple Testing Correction. | Junpei Komiyama, Masakazu Ishihata, Hiroki Arimura, Takashi Nishibayashi, Shin-ichi Minato |
| 2016 | ICML | Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm. | Junpei Komiyama, Junya Honda, Hiroshi Nakagawa |
| 2015 | COLT | Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem. | Junpei Komiyama, Junya Honda, Hisashi Kashima, Hiroshi Nakagawa |
| 2015 | ICML | Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays. | Junpei Komiyama, Junya Honda, Hiroshi Nakagawa |
| 2013 | ACML | Multi-armed Bandit Problem with Lock-up Periods. | Junpei Komiyama, Issei Sato, Hiroshi Nakagawa |