Tadashi Kozuno
16 papers · 2019–2025 · 5 conferences · across top CS/AI conferences
Achievements
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π Academic Marathon (6) π§ Keyword Pioneer π Interdisciplinary Bridge π Conference Polyglot (5) π Cross-Pollinator (11)
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Cross-Pollinator
(11)
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(5)
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Taxonomy Completionist
(25)
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Topic Evolution
ποΈ
Keyword Collector
(62)
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Century Club
(16)
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Trend Setter
π₯
Unstoppable
(7)
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Prolific Year
(5)
Conferences
ICML (6)
NIPS (6)
ICLR (2)
AISTATS (1)
JMLR (1)
Top co-authors
Research topics
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 Pengβs 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