I am a postdoctoral Researcher at EPFL (Ecole Polytechnique Fédérale de Lausanne), in the MLO team, directed by Martin Jaggi.
Before that, I was Ph.D. student in the Sierra Team, which is part of the DI/ENS
(Computer Science Department of École Normale Supérieure). I graduated from Ecole Normale Supérieure de Paris (Ulm) in 2014 and got a Masters Degree in Mathematics,
Probability and Statistics (at Université Paris-Sud, Orsay).
I was supervised by
My main research interests are statistics, optimization, stochastic approximation, high-dimensional learning, non-parametric statistics, scalable kernel methods.
From March to August 2016, I was a visiting scholar researcher at University of California Berkeley, under the supervision of Martin Wainwright.
Thesis defense !
I defended my thesis on Thursday, September 28, at 2.30 pm, at INRIA.
You can download the final version of the manuscript (or here if you want to print it).
You can also have a look at the slides
Non-parametric Stochastic Approximation with Large Step sizes
Published in the Annals of Statistics.
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression
To appear in Journal of Machine Learning Research (JMLR), arXiv:1602.05419 [math.ST].
Bridging the Gap between Constant Step Size Stochastic
Gradient Descent and Markov Chains
December 2016, Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression, with Nicolas Flammarion, Nips, OPT16
December 2015, Adaptativity of stochastic gradient descent, Nips Workshop
2016-2017 : Teaching assistant, Statistics , Master 1 (31NU02MS), University Paris Diderot.
2016-2017 : Teaching assistant, Fundamental Statistics , Master 1 (ULMT42), University Paris Diderot.
2015-2016 : Teaching assistant, Calculus , (MM1), University Paris Diderot.
2014-2015 : Teaching assistant, Linear Algebra , (MM1), University Paris Diderot.
2010-2014 : Oral interrogations in ``Classes préparatoires'' (PC, MP*).
Reviewer for JMLR, AOS, COLT, IEEE, ACM, ICML.
January 2018, Tutoriel d’Optimization, Journées “YSP” organisées par la SFDS, Institut
Decembrer 2017, Stochastic algorithms in Machine learning, Tutoriel at “journée algorithmes stochastiques", Paris Dauphine
November 2017, Stochastic approximation and Markov chains, Invited talk, Télécom Paristech, Paris
February 2017, Scalable methods for Statistics, a short presentation, Cambridge, UK
March 2016, Non parametric stochastic approxiation, UC Berkeley
October 2015, Tradeoffs of learning in Hilbert spaces, ENSAI Rennes
June 2015, Non parametric Stochastic Approximation, Machine Learning Summer School, Tubingen.