← Authors

Tadashi Kozuno

16 papers · 2019–2025 · 5 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (5) πŸƒ Academic Marathon (6) 🐝 Cross-Pollinator (11) 🌈 Renaissance Researcher (5) πŸ—ΊοΈ Taxonomy Completionist (25) 🧬 Topic Evolution πŸ—ƒοΈ Keyword Collector (62) πŸ’Ž Century Club (16) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (7) ⚑ Prolific Year (5)

Conferences

ICML (6) NIPS (6) ICLR (2) AISTATS (1) JMLR (1)

Top co-authors

Michal Valko (8) RΓ©mi Munos (8) Pierre Menard (5) Yunhao Tang (4) Yutaka Matsuo (4) CΓ΄me Fiegel (3) Mark Rowland (3) Vianney Perchet (3) Toshinori Kitamura (2) Olivier Pietquin (2)

Research topics

Reinforcement Learning (1)

Keywords

reinforcement learning (7) value iteration (3) deep reinforcement learning (2) zero-sum game (2) mirror descent (2) off-policy learning (2) multi-step learning (2) imperfect information game (2) policy optimization (2) online mirror descent (2) policy gradient (1) imperfect information (1) markov decision process (1) regret minimization (1) theoretical analysis (1) game theory (1) policy learning (1) minimax optimality (1) mutual information (1) optimal control (1)

Papers

Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form ICLR 2025
The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback ICML 2025
Local and Adaptive Mirror Descents in Extensive-Form Games NIPS 2024
Adapting to game trees in zero-sum imperfect information games ICML 2023
DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm ICML 2023
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice ICML 2023
Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences JMLR 2022
Confident Approximate Policy Iteration for Efficient Local Planning in $q^\pi$-realizable MDPs NIPS 2022
Variational oracle guiding for reinforcement learning ICLR 2022
Learning in two-player zero-sum partially observable Markov games with perfect recall NIPS 2021
Revisiting Peng’s Q($Ξ»$) for Modern Reinforcement Learning ICML 2021
Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning NIPS 2021
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation NIPS 2021
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning ICML 2021
Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning NIPS 2020
Theoretical Analysis of Efficiency and Robustness of Softmax and Gap-Increasing Operators in Reinforcement Learning AISTATS 2019