Principal Sensitivity Analysis. - Sotetsu Koyamada; Masanori Koyama; Ken Nakae; Shin Ishii (PAKDD (1), 2015)

Deep learning of fMRI big data: a novel approach to subject-transfer decoding. - Sotetsu Koyamada; Yumi Shikauchi; Ken Nakae; Masanori Koyama; Shin Ishii (CoRR, 2015)

Neural Sequence Model Training via $α$-divergence Minimization. - Sotetsu Koyamada; Yuta Kikuchi; Atsunori Kanemura; Shin-ichi Maeda; Shin Ishii (CoRR, 2017)

Suphx: Mastering Mahjong with Deep Reinforcement Learning. - Junjie Li; Sotetsu Koyamada; Qiwei Ye; Guoqing Liu; Chao Wang; Ruihan Yang; Li Zhao 007; Tao Qin 001; Tie-Yan Liu; Hsiao-Wuen Hon (CoRR, 2020)

Mjx: A framework for Mahjong AI research. - Sotetsu Koyamada; Keigo Habara; Nao Goto; Shinri Okano; Soichiro Nishimori; Shin Ishii (CoG, 2022)

Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning. - Sotetsu Koyamada; Shinri Okano; Soichiro Nishimori; Yu Murata; Keigo Habara; Haruka Kita; Shin Ishii (NeurIPS, 2023)

Pgx: Hardware-accelerated parallel game simulation for reinforcement learning. - Sotetsu Koyamada; Shinri Okano; Soichiro Nishimori; Yu Murata; Keigo Habara; Haruka Kita; Shin Ishii (CoRR, 2023)

End-to-End Policy Gradient Method for POMDPs and Explainable Agents. - Soichiro Nishimori; Sotetsu Koyamada; Shin Ishii (CoRR, 2023)

A Simple, Solid, and Reproducible Baseline for Bridge Bidding AI. - Haruka Kita; Sotetsu Koyamada; Yotaro Yamaguchi; Shin Ishii (CoG, 2024)

A Batch Sequential Halving Algorithm without Performance Degradation. - Sotetsu Koyamada; Soichiro Nishimori; Shin Ishii (RLJ, 2024)