KOYAMADA Sotetsu
| Graduate School of Economics / Division of Economics | Associate Professor |
| Faculty of Economics / Department of Economics |
Researcher Information
Research activity information
Paper
- 2026, International Conference on Machine Learning (ICML)Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying[Refereed]
- IEEE, 2024, IEEE Conference on Games (CoG), 1 - 4[Refereed]International conference proceedings
- 2024, Reinforcement Learning Journal (RLJ), 5, 2218 - 2232A Batch Sequential Halving Algorithm without Performance Degradation.[Refereed]Scientific journal
- 2023, arXiv, 2304.09769Scientific journal
- 2023, Advances in Neural Information Processing Systems (NeurIPS)Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning.[Refereed]International conference proceedings
- IEEE, 2022, IEEE Conference on Games (CoG), 504 - 507[Refereed]International conference proceedings
- 2020, arXiv, 2003.13590Suphx: Mastering Mahjong with Deep Reinforcement Learning.Scientific journal
- 2017, ICML Workshop on Learning to Generate Natural Language (LGNL)Neural Sequence Model Training via $α$-divergence Minimization.[Refereed]Scientific journal
- 2015, arXiv, 1502.00093Deep learning of fMRI big data: a novel approach to subject-transfer decoding.Scientific journal
- Springer, 2015, Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 621 - 632[Refereed]International conference proceedings
- 2014, Proceedings of the International Conference on Data Mining, Internet Computing, and Big DataConstruction of Subject-independent Brain Decoders for Human fMRI with Deep Learning[Refereed]
Lectures, oral presentations, etc.
- Reinforcement Learning Conference, 2024A Batch Sequential Halving Algorithm without Performance Degradation
- Advances in Neural Information Processing Systems (NeurIPS), 2023Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning
- IEEE Conference on Games, 2022Mjx: A framework for Mahjong AI research
- ICML Workshop on Learning to Generate Natural Language (LGNL), 2017Neural Sequence Model Training via α-divergence Minimization
- Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), May 2015Principal Sensitivity Analysis
- SNSS2015, Mar. 2015Knowledge Discovery for Nonlinear Classifier in Functional Neuroimaging
- AEARU Workshop on Computer Science and Web Technology, Feb. 2015Knowledge Discovery for Nonlinear Classifier in Functional NeuroimagingPoster presentation
- Machine Learning Summer School'15, 2015Principal Sensitivity Analysis
- International Conference on Data Mining, Internet Computing, and Big Data, Nov. 2014Construction of Subject-independent Brain Decoders for Human fMRI with Deep Learning
- Neuro2014, Sep. 2014Learning the subject-independent discriminative features from the large-scale fMRI database
- 脳と心のメカニズム 第1４回冬のワークショップ, Jan. 2014大規模fMRIデータベースからの脳活動表現の学習
