About me
I am a researcher (Inria Starting Faculty Position) in the Scool Team-project at the Inria centre at the University of Lille. I work on sequential machine learning, mostly 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
[October 2025] With Michael Rothgang, I am leading a workshop on Lean for PDEs at the Simons Laufer Mathematical Sciences Institute (SLMath), co-organized by the new Institute for Computer-Aided Reasoning in Mathematics (ICARM).
[July 2025] I am launching the FORMAL Inria exploratory action. It's goal is to formalize bandit and reinforcement learning algorithms and their theory in the Lean theorem prover. A post-doc will join the team in 2026 to work on this project.
[January 2025] I am looking for a post-doc who would join the Inria Scool team and work on bandits and sequential testing (Position filled).
[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] I was awarded an ANR JCJC funding for my project FATE (Frugal and adaptive testing). This project will fund a PhD and a post-doc on bandits and sequential testing.
[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: September 2025. For a perhaps more up-to-date list, see my Google Scholar profile.- The Batch Complexity of Bandit Pure Exploration, ICML 2025 Adrienne Tyunman, RD.
- Best-Arm Identification in Unimodal Bandits, AISTATS 2025 Riccardo Poiani, Marc Jourdan, Emilie Kaufmann, RD.
- Finding good policies in average-reward Markov Decision Processes without prior knowledge, NeurIPS 2024 Adrienne Tyunman, RD, Emilie Kaufmann.
- Optimal Multi-Fidelity Best-Arm Identification, NeurIPS 2024 Riccardo Poiani, RD, Emilie Kaufmann, Alberto Maria Metelli, Marcello Restelli.
- An ε-Best-Arm Identification Algorithm for Fixed-Confidence and Beyond, NeurIPS 2023, Marc Jourdan, RD, Emilie Kaufmann.
- Non-Asymptotic Analysis of a UCB-based Top Two Algorithm, NeurIPS 2023, Marc Jourdan, RD.
- Fast Asymptotically Optimal Algorithms for Non-Parametric Stochastic Bandits, NeurIPS 2023, Dorian Baudry, Fabien Pesquerel, RD, Odalric-Ambrym Maillard.
- On the Existence of a Complexity in Fixed Budget Bandit Identification, Colt 2023. [HTML Version] RD.
- Dealing with Unknown Variances in Best-Arm Identification, ALT 2023, Marc Jourdan, RD, Emilie Kaufmann.
- A formalization of Doob's martingale convergence theorems in mathlib, CPP 2023, Kexing Ying, RD.
- Top Two Algorithms Revisited, NeurIPS 2022, Marc Jourdan, RD, Dorian Baudry, Rianne de Heide, Emilie Kaufmann.
- On Elimination Strategies for Bandit Fixed-Confidence Identification, NeurIPS 2022, Andrea Tirinzoni, RD.
- Choosing Answers in Epsilon-Best-Answer Identification for Linear Bandits, ICML 2022, Marc Jourdan, RD.
- Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits, NeurIPS 2021, Reda Ouhamma, RD, Vianney Perchet, Pierre Gaillard.
- Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification, NeurIPS 2021, Clémence Reda, Andrea Tirinzoni, RD.
- Structure Adaptive Algorithms for Stochastic Bandits, ICML 2020, RD, Han Shao, Wouter M. Koolen.
- Gamification of Pure Exploration for Linear Bandits, ICML 2020, RD, Pierre Ménard, Xuedong Shang, Michal Valko.
- Non-asymptotic pure exploration by solving games, NeurIPS 2019, RD, Wouter M. Koolen, Pierre Ménard.
- Pure exploration with multiple correct answers, NeurIPS 2019, RD, Wouter M. Koolen.
- Bridging the gap between regret minimization and best arm identification, with application to A/B tests, AISTATS 2019, RD, Thomas Nedelec, Clément Calauzènes, Vianney Perchet.
- Bandits with side observations: bounded vs. logarithmic regret, UAI 2018, RD, Evrard Garcelon, Vianney Perchet.
- Combinatorial semi-bandit with known covariance, NIPS 2016, RD, Vianney Perchet.
- Anytime optimal algorithms in stochastic multi-armed bandits, ICML 2016 RD, Vianney Perchet.
Mathlib contributions
I am a contributor and a maintainer of Mathlib, the mathematics library of the Lean theorem prover. Here are some of my contributions, mostly related to probability:- A definition of Gaussian distributions in Banach spaces. Coming soon: Fernique's theorem and the Cameron-Martin theorem.
- The Kullback-Leibler divergence. Coming soon: other information divergences, notably related to the risk of statistical experiments.
- Sub-Gaussian random variables, moment generating functions and concentration inequalities.
- Independence and conditional independence.
- Probability transition kernels and their compositions. Disintegration of kernels and definitions of conditional distributions and posterior distributions. Radon-Nikodym theorem for kernels.
- Martingales and stopping times: Doob's martingale convergence theorems, optional stopping, optional sampling. See A formalization of Doob's martingale convergence theorems in mathlib, CPP 2023, Kexing Ying, RD.
- Conditional expectations.
- Lp spaces, the fact that they are Banach spaces and that L2 is a Hilbert space.
Teaching
Current courses- Centrale Lille, fall 2024 - Sequential learning
- ENS Paris-Saclay (Master MVA), spring 2025 - Sequential learning, with Pierre Gaillard
- ENS Paris-Saclay (Master MVA) - Sequential learning - 2021, 2022, 2023
- Centrale Lille - Sequential learning - 2022, 2023
- Université de Lille (L3 MIASHS) - Science des données 3 - 2022, 2023
- Université Paris Diderot - Practical sessions for various courses - 2016 to 2018. Courses: Analyse et algèbre 2 (L1 Physique), Raisonnement mathématique (L1 Informatique, L1 MIASHS), Probabilités (L2 Math-Info), Equation différentielles pour la biologie, Probabilités (L2 Math).
Collaborators
PhD students- Adrienne Tyunman, co-advised with Emilie Kaufmann, since October 2023.
- Marc Jourdan, co-advised with Emilie Kaufmann, October 2021 - June 2024.
- Andrea Tirinzoni -- 2021
- Lorenzo Luccioli, April-September 2024
- Adrienne Tyunman, co-supervised with Emilie Kaufmann, April-August 2023
- Marc Jourdan, April-August 2021