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ORCIDSoichiro Nishimori
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2020 – today
- 2026
[j2]Soichiro Nishimori, Yu-Jie Zhang, Thanawat Lodkaew, Masashi Sugiyama:
On Symmetric Losses for Policy Optimization with Noisy Preferences. Trans. Mach. Learn. Res. 2026 (2026)- [i16]Shinnosuke Ono, Johannes Ackermann, Soichiro Nishimori, Takashi Ishida, Masashi Sugiyama:
Mitigating Reward Hacking in RLHF via Advantage Sign Robustness. CoRR abs/2604.02986 (2026) - [i15]Soichiro Nishimori, Shinri Okano, Keigo Habara, Sotetsu Koyamada, Eason Yu, Masashi Sugiyama:
Mahjax: A GPU-Accelerated Mahjong Simulator for Reinforcement Learning in JAX. CoRR abs/2605.20577 (2026) - [i14]Bingkui Tong, Junpei Komiyama, Soichiro Nishimori, Paavo Parmas:
Finite-Time Regret Analysis of Retry-Aware Bandits. CoRR abs/2605.20854 (2026) - [i13]Soichiro Nishimori, Paavo Parmas, Sotetsu Koyamada, Tadashi Kozuno, Toshinori Kitamura, Shin Ishii, Yutaka Matsuo:
Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying. CoRR abs/2606.00151 (2026) - [i12]Soichiro Nishimori, Paavo Parmas:
Retry Policy Gradients in Continuous Action Spaces. CoRR abs/2606.05888 (2026) - [i11]Shota Takashiro, Soichiro Nishimori, Paavo Parmas, Yongmin Kim, Kohsei Matsutani, Gouki Minegishi, Yusuke Iwasawa, Takeshi Kojima, Yutaka Matsuo:
On Advantage Estimates for Max@K Policy Gradients. CoRR abs/2606.06080 (2026) - [i10]Paavo Parmas, Yongmin Kim, Kohsei Matsutani, Shota Takashiro, Soichiro Nishimori, Takeshi Kojima, Yusuke Iwasawa, Yutaka Matsuo:
OrderGrad: Optimizing Beyond the Mean with Order-Statistic Policy Gradient Estimation. CoRR abs/2606.06096 (2026) - [i9]Thanawat Lodkaew, Johannes Ackermann, Soichiro Nishimori, Nontawat Charoenphakdee, Masashi Sugiyama, Takashi Ishida:
Do Coding Agents Deceive Us? Detecting and Preventing Cheating via Capped Evaluation with Randomized Tests. CoRR abs/2606.07379 (2026) - [i8]Ku Onoda, Paavo Parmas, Hiroki Furuta, Soichiro Nishimori, Yuta Oshima, Shohei Taniguchi, Yutaka Matsuo:
Multi-Axis Max@K Reinforcement Learning for Representative Diversity in Text-to-Image Generation. CoRR abs/2607.14962 (2026) - 2025
- [i7]Soichiro Nishimori, Yu-Jie Zhang, Thanawat Lodkaew, Masashi Sugiyama:
On Symmetric Losses for Robust Policy Optimization with Noisy Preferences. CoRR abs/2505.24709 (2025) - [i6]Yuting Tang, Yivan Zhang
, Johannes Ackermann, Yu-Jie Zhang, Soichiro Nishimori, Masashi Sugiyama:
Recursive Reward Aggregation. CoRR abs/2507.08537 (2025) - 2024
- [j1]Sotetsu Koyamada, Soichiro Nishimori, Shin Ishii:
A Batch Sequential Halving Algorithm without Performance Degradation. RLJ 5: 2218-2232 (2024) - [i5]Toshinori Kitamura, Tadashi Kozuno, Masahiro Kato, Yuki Ichihara, Soichiro Nishimori, Akiyoshi Sannai, Sho Sonoda, Wataru Kumagai, Yutaka Matsuo:
A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC Guarantees. CoRR abs/2401.17780 (2024) - [i4]Soichiro Nishimori, Xin-Qiang Cai, Johannes Ackermann, Masashi Sugiyama:
Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two Domains. CoRR abs/2404.07465 (2024) - [i3]Sotetsu Koyamada, Soichiro Nishimori, Shin Ishii:
A Batch Sequential Halving Algorithm without Performance Degradation. CoRR abs/2406.00424 (2024) - 2023
- [c2]Sotetsu Koyamada, Shinri Okano, Soichiro Nishimori, Yu Murata, Keigo Habara, Haruka Kita, Shin Ishii:
Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning. NeurIPS 2023 - [i2]Sotetsu Koyamada, Shinri Okano, Soichiro Nishimori, Yu Murata, Keigo Habara, Haruka Kita, Shin Ishii:
Pgx: Hardware-accelerated parallel game simulation for reinforcement learning. CoRR abs/2303.17503 (2023) - [i1]Soichiro Nishimori, Sotetsu Koyamada, Shin Ishii:
End-to-End Policy Gradient Method for POMDPs and Explainable Agents. CoRR abs/2304.09769 (2023) - 2022
- [c1]Sotetsu Koyamada, Keigo Habara, Nao Goto, Shinri Okano, Soichiro Nishimori, Shin Ishii:
Mjx: A framework for Mahjong AI research. CoG 2022: 504-507
Coauthor Index
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