Pierre Gaillard
30 papers · 2012–2026 · 6 venues · across top CS/AI venues
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Achievements
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🐣 Hot Topic Early Bird 🌍 Conference Polyglot · Silver 🏃 Academic Marathon · Half 🌉 Interdisciplinary Bridge · Silver 🗺️ Taxonomy Completionist (33)
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Deep Specialist
(14)
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Keyword Collector
(131)
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Trend Setter
💎
Century Club · Bronze
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Unstoppable · Silver
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Research topics
Machine Learning
(27)
Optimization & Theory
(24)
Mathematics & Optimization
(19)
Optimization
(19)
Learning Types
(16)
Core Methods
(10)
Artificial Intelligence
(7)
Core AI
(6)
Reinforcement Learning
(2)
Methods
(2)
Data Science & Analytics
(1)
Application Areas
(1)
Applications
(1)
Learning Paradigms
(1)
Keywords
regret bound
(19)
online learning
(13)
multi-armed bandit
(8)
stochastic optimization
(7)
nonparametric regression
(3)
contextual bandit
(3)
dueling bandit
(3)
semi-bandit feedback
(2)
sleeping bandit
(2)
logistic regression
(2)
online algorithm
(2)
adversarial learning
(2)
mirror descent
(2)
regret minimization
(2)
stochastic gradient descent
(2)
convergence rate
(2)
empirical risk minimization
(1)
computational efficiency
(1)
policy optimization
(1)
covariance estimation
(1)
Papers
Enjoying Non-linearity in Multinomial Logistic Bandits: A Minimax-Optimal Algorithm
ALT 2026
Leveraging Similarities in Multi-Armed Bandits
COLT 2026
Online Markov Decision Processes with Terminal Law Constraints
ALT 2026
Finally Rank-Breaking Conquers MNL Bandits: Optimal and Efficient Algorithms for MNL Assortment
ICLR 2025
Logarithmic Regret for Unconstrained Submodular Maximization Stochastic Bandit
ALT 2025
Minimax-optimal and Locally-adaptive Online Nonparametric Regression
ALT 2025
Online Episodic Convex Reinforcement Learning
ICML 2025
Efficient Model-Based Concave Utility Reinforcement Learning through Greedy Mirror Descent
AISTATS 2024
MetaCURL: Non-stationary Concave Utility Reinforcement Learning
NeurIPS 2024
Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits
NeurIPS 2024
One Arrow, Two Kills: A Unified Framework for Achieving Optimal Regret Guarantees in Sleeping Bandits
AISTATS 2023
Sequential Counterfactual Risk Minimization
ICML 2023
Efficient Kernelized UCB for Contextual Bandits
AISTATS 2022
Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences
ICML 2022
Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms
NeurIPS 2021
Dueling Bandits with Adversarial Sleeping
NeurIPS 2021
Mixability made efficient: Fast online multiclass logistic regression
NeurIPS 2021
Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits
NeurIPS 2021
Efficient improper learning for online logistic regression
COLT 2020
Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards
ICML 2020
Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model
NeurIPS 2020
Efficient online learning with kernels for adversarial large scale problems
NeurIPS 2019
Target Tracking for Contextual Bandits: Application to Demand Side Management
ICML 2019
Uniform regret bounds over R^d for the sequential linear regression problem with the square loss
ALT 2019
Efficient online algorithms for fast-rate regret bounds under sparsity
NeurIPS 2018
Algorithmic Chaining and the Role of Partial Feedback in Online Nonparametric Learning
COLT 2017
Sparse Accelerated Exponential Weights
AISTATS 2017
A Chaining Algorithm for Online Nonparametric Regression
COLT 2015
A second-order bound with excess losses
COLT 2014
Mirror Descent Meets Fixed Share (and feels no regret)
NeurIPS 2012