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<!DOCTYPE HTML>
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<title>Rémy Degenne</title>
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<h1 id="logo"><a href="#">Rémy Degenne</a></h1>
<p>firstname.lastname(at)inria.fr</p>
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<nav id="nav">
<ul>
<li><a href="#one" class="active">About</a></li>
<li><a href="#two">Publications</a></li>
<li><a href="#three">Teaching</a></li>
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<h3>About me</h3>
</header>
<p> I am a researcher (Inria Starting Faculty Position) in the <a href="https://team.inria.fr/scool/">Scool Team-project</a> at the <a href="https://www.inria.fr/en/inria-centre-university-lille">Inria centre at the University of Lille</a>. 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.
<p>I am a maintainer of <a href="https://leanprover-community.github.io/">the mathematical library <i>Mathlib</i> for the Lean theorem prover</a>.</br>
If you are looking for something to contribute to Mathlib related to probability, here is a <a href="https://remydegenne.github.io/Lean_projects">list of projects</a>.
<p>In 2020 I was a post-doctoral researcher at Inria Paris, in the <a href="https://www.di.ens.fr/sierra/">SIERRA Team</a>. In 2018-2019 I spent a year in the <a href="https://www.cwi.nl/research/groups/machine-learning">Machine Learning group at CWI Amsterdam</a>, working with <a href="https://wouterkoolen.info/">Wouter M. Koolen</a>. From 2015 to 2019 I was a PhD student under the supervision of <a href="https://sites.google.com/site/vianneyperchet/">Vianney Perchet</a> at the <a href="http://www.cmla.ens-cachan.fr/">CMLA research center of Ecole Normale Supérieure Paris-Saclay</a> and the <a href="http://www.lpsm.paris">LPSM</a> lab at <a href="https://u-paris.fr/en/"> Université Paris Cité</a>.
<header class="major">
<h3>News</h3>
</header>
<p>
<b style="color:#B2B2B2";>[November 2023]</b> 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 <a href="https://terrytao.wordpress.com/2023/11/18/formalizing-the-proof-of-pfr-in-lean4-using-blueprint-a-short-tour/">this post on Terence Tao's blog</a> for a tour of the project.
</p>
<p>
<b style="color:#B2B2B2";>[2022]</b> My ANR JCJC project proposal FATE (Frugal and adaptive testing) was accepted.
</p>
<p>
<b style="color:#B2B2B2";>[July 2022]</b> I co-organize the <a href="https://cfol-workshop.github.io/"><b>Complex Feedback in Online Learning</b></a> 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.
</p>
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</section>
<!-- Two -->
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<h3>Publications</h3>
Last updated: December 2023.
<ul ><!--class="feature-icons">-->
<li ><b><a href="https://arxiv.org/abs/2305.16041">An ε-Best-Arm Identification Algorithm for Fixed-Confidence and Beyond</a></b>, NeurIPS 2023,</br>
with <a href="https://thirsty-goodall-d21ccc.netlify.app/">Marc Jourdan</a> and <a href="https://emiliekaufmann.github.io/">Emilie Kaufmann</a>.
<li ><b><a href="https://arxiv.org/abs/2210.05431">Non-Asymptotic Analysis of a UCB-based Top Two Algorithm</a></b>, NeurIPS 2023,</br>
with <a href="https://thirsty-goodall-d21ccc.netlify.app/">Marc Jourdan</a>.
<li ><b><a href="https://inria.hal.science/hal-04337742/">Fast Asymptotically Optimal Algorithms for Non-Parametric Stochastic Bandits</a></b>, NeurIPS 2023,</br>
with <a href="https://dbaudry.github.io/">Dorian Baudry</a>, Fabien Pesquerel and Odalric-Ambrym Maillard.
<li ><b><a href="https://arxiv.org/abs/2303.09468">On the Existence of a Complexity in Fixed Budget Bandit Identification</a></b>, Colt 2023.</br> <a href="https://remydegenne.github.io/papers_html/no_complexity_fb/fixed_budget/index.html">[HTML Version]</a></br>
<li ><b><a href="https://arxiv.org/abs/2210.00974">Dealing with Unknown Variances in Best-Arm Identification</a></b>, ALT 2023,</br>
with <a href="https://thirsty-goodall-d21ccc.netlify.app/">Marc Jourdan</a> and <a href="https://emiliekaufmann.github.io/">Emilie Kaufmann</a>.
<li ><b><a href="https://arxiv.org/abs/2212.05578">A formalization of Doob's martingale convergence theorems in mathlib</a></b>, CPP 2023,</br>
with Kexing Ying.
<li ><b><a href="https://arxiv.org/abs/2206.05979">Top Two Algorithms Revisited</a></b>, NeurIPS 2022,</br>
with <a href="https://thirsty-goodall-d21ccc.netlify.app/">Marc Jourdan</a>, <a href="https://dbaudry.github.io/">Dorian Baudry</a>, Rianne de Heide and <a href="https://emiliekaufmann.github.io/">Emilie Kaufmann</a>.
<li ><b><a href="https://arxiv.org/abs/2205.10936">On Elimination Strategies for Bandit Fixed-Confidence Identification</a></b>, NeurIPS 2022,</br>
with <a href="https://scholar.google.com/citations?user=MmW0yrwAAAAJ&hl=en">Andrea Tirinzoni</a>.
<li ><b><a href="https://arxiv.org/abs/2206.04456">Choosing Answers in Epsilon-Best-Answer Identification for Linear Bandits</a></b>, ICML 2022,</br>
with <a href="https://thirsty-goodall-d21ccc.netlify.app/">Marc Jourdan</a>.
<li ><b><a href="https://arxiv.org/abs/2110.09133">Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits</a></b>, NeurIPS 2021,</br>
with <a href="https://scholar.google.com/citations?hl=en&user=DYe2NmQAAAAJ">Reda Ouhamma</a>, <a href="https://vianney.ai/">Vianney Perchet</a> and <a href="http://pierre.gaillard.me/index.html">Pierre Gaillard</a>.
<li ><b><a href="https://arxiv.org/abs/2111.01479">Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification</a></b>, NeurIPS 2021,</br>
with <a href="https://clreda.github.io/">Clémence Reda</a> and <a href="https://andreatirinzoni.github.io/">Andrea Tirinzoni</a>.
<li ><b><a href="https://arxiv.org/abs/2007.00969">Structure Adaptive Algorithms for Stochastic Bandits</a></b>, ICML 2020,</br>
with <a href="https://sites.google.com/view/hanshao">Han Shao</a> and <a href="https://wouterkoolen.info/">Wouter M. Koolen</a>.
<li ><b><a href="https://arxiv.org/abs/2007.00953">Gamification of Pure Exploration for Linear Bandits</a></b>, ICML 2020,</br>
with <a href="https://perso.math.univ-toulouse.fr/pmenard/">Pierre Ménard</a>, <a href="https://xuedong.github.io/">Xuedong Shang</a> and <a href="http://researchers.lille.inria.fr/~valko/hp/">Michal Valko</a>.
<li ><b><a href="https://arxiv.org/abs/1906.10431">Non-asymptotic pure exploration by solving games</a></b>, NeurIPS 2019,</br>
with <a href="https://wouterkoolen.info/">Wouter M. Koolen</a> and <a href="https://perso.math.univ-toulouse.fr/pmenard/">Pierre Ménard</a>.</li>
<li ><b><a href="https://arxiv.org/abs/1902.03475">Pure exploration with multiple correct answers</a></b>, NeurIPS 2019,</br>
with <a href="https://wouterkoolen.info/">Wouter M. Koolen</a>.</li>
<li ><b><a href="https://arxiv.org/abs/1810.04088">Bridging the gap between regret minimization and best arm identification, with application to A/B tests</a></b>, AISTATS 2019,</br>
with Thomas Nedelec, Clément Calauzènes and <a href="https://vianney.ai/">Vianney Perchet</a>.</li>
<li ><b><a href="https://arxiv.org/abs/1807.03558">Bandits with side observations: bounded vs. logarithmic regret</a></b>, UAI 2018,</br>
with Evrard Garcelon and <a href="https://vianney.ai/">Vianney Perchet</a>.</li>
<li ><b><a href="https://papers.nips.cc/paper/6137-combinatorial-semi-bandit-with-known-covariance.pdf">Combinatorial semi-bandit with known covariance</a></b>, NIPS 2016,</br>
with <a href="https://vianney.ai/">Vianney Perchet</a>.</li>
<li ><b><a href="http://www.jmlr.org/proceedings/papers/v48/degenne16.pdf">Anytime optimal algorithms in stochastic multi-armed bandits</a></b>, ICML 2016</br>
with <a href="https://vianney.ai/">Vianney Perchet</a>.</li>
</ul>
</div>
</section>
<!-- Three-->
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<div class="container">
<h3>Teaching</h3>
Centrale Lille
<ul>
<li><b>Fall 2024</b> - <a href="https://remydegenne.github.io/SL_2024">Sequential learning</a></li>
<li><b>Fall 2023</b> - <a href="https://remydegenne.github.io/SL_2023">Sequential learning</a></li>
<li><b>Fall 2022</b> - <a href="https://remydegenne.github.io/SL_2022">Sequential learning</a></li>
</ul>
Université de Lille
<ul>
<li><b>Spring 2023</b> - Science des données 3, L3 MIASHS</li>
<li><b>Spring 2022</b> - Science des données 3, L3 MIASHS</li>
</ul>
ENS Paris-Saclay (Master MVA)
<ul>
<li><b>Spring 2025</b> - <a href="http://pierre.gaillard.me/teaching/mva.html">Sequential learning</a>, with <a href="http://pierre.gaillard.me/">Pierre Gaillard</a></li>
<li><b>Spring 2023</b> - <a href="http://pierre.gaillard.me/teaching/mva.html">Sequential learning</a>, with <a href="http://pierre.gaillard.me/">Pierre Gaillard</a></li>
<li><b>Spring 2022</b> - <a href="http://pierre.gaillard.me/teaching/mva.html">Sequential learning</a>, with <a href="http://pierre.gaillard.me/">Pierre Gaillard</a></li>
<li><b>Spring 2021</b> - <a href="http://pierre.gaillard.me/teaching/mva.html">Sequential learning</a>, with <a href="http://pierre.gaillard.me/">Pierre Gaillard</a></li>
</ul>
Université Paris Diderot
<ul ><!--class="feature-icons">-->
<li ><b>Spring 2018</b> - Probabilités, L2 Math.</li>
<li ><b>Fall 2018</b> - Equation différentielles pour la biologie.</li>
<li ><b>Spring 2017</b> - Probabilités, L2 Math-Info.</li>
<li ><b>Fall 2017</b> - Raisonnement mathématique, L1 Informatique, L1 MIASHS.</li>
<li ><b>Spring 2016</b> - Analyse et algèbre 2, L1 Physique.</li>
</ul>
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