About me
I am a researcher in the Scool Team-project at Inria centre at the University of Lille. I work mostly on bandit theory and I am interested in all forms of online and reinforcement learning as well as statistics and optimization.
I am a maintainer of the mathematical library Mathlib for the Lean theorem prover. If you are looking for something to contribute to Mathlib related to probability, here is a list of projects.
In 2020 I was a post-doctoral researcher at Inria Paris, in the SIERRA Team. In 2018-2019 I spent a year in the Machine Learning group at CWI Amsterdam, working with Wouter M. Koolen. From 2015 to 2019 I was a PhD student under the supervision of Vianney Perchet at the CMLA research center of Ecole Normale Supérieure Paris-Saclay and the LPSM lab at Université Paris Cité.
News
[November 2023] I am contributing to the formalization of the polynomial Freiman-Ruzsa conjecture in Lean. This is a project led by Terence Tao, who recently proved this result with W. T. Gowers, Ben Green and Freddie Manners. See this post on Terence Tao's blog for a tour of the project.
[2022] My ANR JCJC project proposal FATE (Frugal and adaptive testing) was accepted.
[July 2022] I co-organize the Complex Feedback in Online Learning workshop at ICML 2022 (Baltimore, July 23). This workshop aims to present a broad overview of the feedback types being actively researched in sequential learning (reinforcement learning, bandits, games...), highlight recent advances and provide a networking forum for researchers and practitioners.
Publications
Last updated: December 2023.- An ε-Best-Arm Identification Algorithm for Fixed-Confidence and Beyond, NeurIPS 2023, with Marc Jourdan and Emilie Kaufmann.
- Non-Asymptotic Analysis of a UCB-based Top Two Algorithm, NeurIPS 2023, with Marc Jourdan.
- Fast Asymptotically Optimal Algorithms for Non-Parametric Stochastic Bandits, NeurIPS 2023, with Dorian Baudry, Fabien Pesquerel and Odalric-Ambrym Maillard.
- On the Existence of a Complexity in Fixed Budget Bandit Identification, Colt 2023. [HTML Version]
- Dealing with Unknown Variances in Best-Arm Identification, ALT 2023, with Marc Jourdan and Emilie Kaufmann.
- A formalization of Doob's martingale convergence theorems in mathlib, CPP 2023, with Kexing Ying.
- Top Two Algorithms Revisited, NeurIPS 2022, with Marc Jourdan, Dorian Baudry, Rianne de Heide and Emilie Kaufmann.
- On Elimination Strategies for Bandit Fixed-Confidence Identification, NeurIPS 2022, with Andrea Tirinzoni.
- Choosing Answers in Epsilon-Best-Answer Identification for Linear Bandits, ICML 2022, with Marc Jourdan.
- Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits, NeurIPS 2021, with Reda Ouhamma, Vianney Perchet and Pierre Gaillard.
- Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification, NeurIPS 2021, with Clémence Reda and Andrea Tirinzoni.
- Structure Adaptive Algorithms for Stochastic Bandits, ICML 2020, with Han Shao and Wouter M. Koolen.
- Gamification of Pure Exploration for Linear Bandits, ICML 2020, with Pierre Ménard, Xuedong Shang and Michal Valko.
- Non-asymptotic pure exploration by solving games, NeurIPS 2019, with Wouter M. Koolen and Pierre Ménard.
- Pure exploration with multiple correct answers, NeurIPS 2019, with Wouter M. Koolen.
- Bridging the gap between regret minimization and best arm identification, with application to A/B tests, AISTATS 2019, with Thomas Nedelec, Clément Calauzènes and Vianney Perchet.
- Bandits with side observations: bounded vs. logarithmic regret, UAI 2018, with Evrard Garcelon and Vianney Perchet.
- Combinatorial semi-bandit with known covariance, NIPS 2016, with Vianney Perchet.
- Anytime optimal algorithms in stochastic multi-armed bandits, ICML 2016 with Vianney Perchet.
Teaching
Centrale Lille- Fall 2023 - Sequential learning
- Fall 2022 - Sequential learning
- Spring 2023 - Science des données 3, L3 MIASHS
- Spring 2022 - Science des données 3, L3 MIASHS
- Spring 2023 - Sequential learning, with Pierre Gaillard
- Spring 2022 - Sequential learning, with Pierre Gaillard
- Spring 2021 - Sequential learning, with Pierre Gaillard
- Spring 2018 - Probabilités, L2 Math.
- Fall 2018 - Equation différentielles pour la biologie.
- Spring 2017 - Probabilités, L2 Math-Info.
- Fall 2017 - Raisonnement mathématique, L1 Informatique, L1 MIASHS.
- Spring 2016 - Analyse et algèbre 2, L1 Physique.