I am a third year 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 am supervised by Francis Bach. 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.

My CV.

Thesis defense !

I will be defending my thesis on Thursday, September 28, at 2.30 pm, at INRIA. Please email me if you plan to join.

You can download the current version of the manuscript (or here if you want to print it).


Non-parametric Stochastic Approximation with Large Step sizes
Aymeric Dieuleveut and Francis Bach.
Published in the Annals of Statistics.
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression
Aymeric Dieuleveut, Nicolas Flammarion and Francis Bach
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
Aymeric Dieuleveut, Alain Durmus and Francis Bach
arXiv:1707.06386 [math.ST].


December 2016, Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression, with Nicolas Flammarion, Nips, OPT16 . [abstract]
December 2015, Adaptativity of stochastic gradient descent, Nips Workshop. [abstract][slides]


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.


February 2017, Scalable methods for Statistics, a short presentation, Cambridge, UK . [slides]
March 2016, Non parametric stochastic approxiation, UC Berkeley.
October 2015, Tradeoffs of learning in Hilbert spaces, ENSAI Rennes. [slides]
June 2015, Non parametric Stochastic Approximation, Machine Learning Summer School, Tubingen.