Kozuno, Tadashi

20 publications

ICLR 2025 Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form Toshinori Kitamura, Tadashi Kozuno, Wataru Kumagai, Kenta Hoshino, Yohei Hosoe, Kazumi Kasaura, Masashi Hamaya, Paavo Parmas, Yutaka Matsuo

NeurIPS 2025 Provably Efficient RL Under Episode-Wise Safety in Constrained MDPs with Linear Function Approximation Toshinori Kitamura, Arnob Ghosh, Tadashi Kozuno, Wataru Kumagai, Kazumi Kasaura, Kenta Hoshino, Yohei Hosoe, Yutaka Matsuo

NeurIPS 2025 Self Iterative Label Refinement via Robust Unlabeled Learning Hikaru Asano, Tadashi Kozuno, Yukino Baba

ICML 2025 The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback Côme Fiegel, Pierre Menard, Tadashi Kozuno, Michal Valko, Vianney Perchet

ECCVW 2024 Hand2Any: Hand-to-Any Motion Mapping with Few-Shot User Adaptation for Avatar Manipulation Riku Shinohara, Atsushi Hashimoto, Tadashi Kozuno, Shigeo Yoshida, Yutaro Hirao, Monica Perusquía-Hernández, Hideaki Uchiyama, Kiyoshi Kiyokawa

NeurIPS 2024 Local and Adaptive Mirror Descents in Extensive-Form Games Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Rémi Munos, Vianney Perchet, Michal Valko

ICML 2023 Adapting to Game Trees in Zero-Sum Imperfect Information Games Côme Fiegel, Pierre Menard, Tadashi Kozuno, Remi Munos, Vianney Perchet, Michal Valko

ICML 2023 DoMo-AC: Doubly Multi-Step Off-Policy Actor-Critic Algorithm Yunhao Tang, Tadashi Kozuno, Mark Rowland, Anna Harutyunyan, Remi Munos, Bernardo Avila Pires, Michal Valko

ICML 2023 Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard, Michal Valko, Wenhao Yang, Jincheng Mei, Pierre Menard, Mohammad Gheshlaghi Azar, Remi Munos, Olivier Pietquin, Matthieu Geist, Csaba Szepesvari, Wataru Kumagai, Yutaka Matsuo

NeurIPS 2022 Confident Approximate Policy Iteration for Efficient Local Planning in $q^\pi$-Realizable MDPs Gellért Weisz, András György, Tadashi Kozuno, Csaba Szepesvari

JMLR 2022 Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences Alan Chan, Hugo Silva, Sungsu Lim, Tadashi Kozuno, A. Rupam Mahmood, Martha White

TMLR 2022 No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL Han Wang, Archit Sakhadeo, Adam M White, James M Bell, Vincent Liu, Xutong Zhao, Puer Liu, Tadashi Kozuno, Alona Fyshe, Martha White

ICLR 2022 Variational Oracle Guiding for Reinforcement Learning Dongqi Han, Tadashi Kozuno, Xufang Luo, Zhao-Yun Chen, Kenji Doya, Yuqing Yang, Dongsheng Li

NeurIPS 2021 Co-Adaptation of Algorithmic and Implementational Innovations in Inference-Based Deep Reinforcement Learning Hiroki Furuta, Tadashi Kozuno, Tatsuya Matsushima, Yutaka Matsuo, Shixiang Gu

NeurIPS 2021 Learning in Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall Tadashi Kozuno, Pierre Ménard, Remi Munos, Michal Valko

ICML 2021 Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, Shixiang Shane Gu

ICML 2021 Revisiting Peng’s Q($λ$) for Modern Reinforcement Learning Tadashi Kozuno, Yunhao Tang, Mark Rowland, Remi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel

NeurIPS 2021 Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation Yunhao Tang, Tadashi Kozuno, Mark Rowland, Remi Munos, Michal Valko

NeurIPS 2020 Leverage the Average: An Analysis of KL Regularization in Reinforcement Learning Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Remi Munos, Matthieu Geist

AISTATS 2019 Theoretical Analysis of Efficiency and Robustness of SoftMax and Gap-Increasing Operators in Reinforcement Learning Tadashi Kozuno, Eiji Uchibe, Kenji Doya