Pierre Menard
29 papers · 2017–2025 · 7 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (36) π Academic Marathon Β· Sprint π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
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Dynamic Duo
(22)
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Deep Specialist
(12)
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Conference Pioneer
π₯
Unstoppable Β· Bronze
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Prolific Year Β· Prolific
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Century Club Β· Bronze
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Keyword Collector
(116)
Top co-authors
Research topics
Machine Learning
(24)
Optimization & Theory
(21)
Reinforcement Learning
(17)
Learning Types Β· Machine Learning
(17)
Methods
(13)
Artificial Intelligence
(8)
Mathematics & Optimization
(8)
Core AI
(8)
Optimization
(8)
Applications
(5)
Deep Learning
(2)
Core Methods
(2)
Learning Types Β· Deep Learning
(2)
Mathematics
(1)
Keywords
regret bound
(10)
sample complexity
(9)
multi-armed bandit
(9)
markov decision process
(7)
upper confidence bound
(5)
reinforcement learning
(5)
game theory
(3)
online learning
(3)
best arm identification
(3)
regret minimization
(3)
stochastic optimization
(3)
pure exploration
(3)
posterior sampling
(2)
active learning
(2)
zero-sum game
(2)
nash equilibrium
(2)
bayesian inference
(2)
mirror descent
(2)
arm selection
(2)
online mirror descent
(2)
Papers
The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback
ICML 2025
Demonstration-Regularized RL
ICLR 2024
Local and Adaptive Mirror Descents in Extensive-Form Games
NeurIPS 2024
Adapting to game trees in zero-sum imperfect information games
ICML 2023
Fast Rates for Maximum Entropy Exploration
ICML 2023
Model-free Posterior Sampling via Learning Rate Randomization
NeurIPS 2023
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
ICML 2023
Adaptive Multi-Goal Exploration
AISTATS 2022
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses
ICML 2022
KL-UCB-Switch: Optimal Regret Bounds for Stochastic Bandits from Both a Distribution-Dependent and a Distribution-Free Viewpoints
JMLR 2022
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
NeurIPS 2022
Adaptive Reward-Free Exploration
ALT 2021
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces
AISTATS 2021
Bandits with many optimal arms
NeurIPS 2021
Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited
ALT 2021
Fast active learning for pure exploration in reinforcement learning
ICML 2021
Indexed Minimum Empirical Divergence for Unimodal Bandits
NeurIPS 2021
Kernel-Based Reinforcement Learning: A Finite-Time Analysis
ICML 2021
Learning in two-player zero-sum partially observable Markov games with perfect recall
NeurIPS 2021
Problem Dependent View on Structured Thresholding Bandit Problems
ICML 2021
UCB Momentum Q-learning: Correcting the bias without forgetting
ICML 2021
A single algorithm for both restless and rested rotting bandits
AISTATS 2020
Fixed-confidence guarantees for Bayesian best-arm identification
AISTATS 2020
Gamification of Pure Exploration for Linear Bandits
ICML 2020
Planning in Markov Decision Processes with Gap-Dependent Sample Complexity
NeurIPS 2020
The Influence of Shape Constraints on the Thresholding Bandit Problem
COLT 2020
Non-Asymptotic Pure Exploration by Solving Games
NeurIPS 2019
Planning in entropy-regularized Markov decision processes and games
NeurIPS 2019
A minimax and asymptotically optimal algorithm for stochastic bandits
ALT 2017