Kilian Fatras

PhD Student
IRISA-INRIA Rennes & Obelix

Welcome to my personal website !

I am a PhD candidate under the supervision of Pr. Nicolas Courty and Pr. Rémi Flamary at IRISA-INRIA Panama and Obelix. My research focuses on optimal transport, machine learning and optimization.

I graduated from both Ecole Polytechnique and ENSTA ParisTech in applied mathematics and machine learning. I was also an exchange student at UC Berkeley during the fall of 2018. For my final master internship, I was an intern at the University of British Columbia under the supervision of Pr. Mark Schmidt.

You can find my resume here.

My work focuses on optimization for machine learning and the interaction between optimal transport and machine learning.

Optimization

[NEW!] Proximal Splitting Meets Variance Reduction
Fabian Pedregosa, Kilian Fatras and Mattia Casotto.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2019

Abstract: Despite the rise to fame of incremental variance-reduced methods in recent years, their use in nonsmooth optimization is still limited to few simple cases. This is due to the fact that existing methods require to evaluate the proximity operator for the nonsmooth terms, which can be a costly operation for complex penalties. In this work we introduce two variance-reduced incremental methods based on SAGA and SVRG that can efficiently take into account complex penalties which can be expressed as a sum of proximal terms. This includes penalties such as total variation, group lasso with overlap and trend filtering, to name a few. Furthermore, we also develop sparse variants of the proposed algorithms which can take advantage of sparsity in the input data. Like other incremental methods, it only requires to evaluate the gradient of a single sample per iteration, and so is ideally suited for large scale applications. We provide a convergence rate analysis for the proposed methods and show that they converge with a fixed step-size, achieving in some cases the same asymptotic rate as their full gradient variants. Empirical benchmarks on 3 different datasets illustrate the practical advantages of the proposed methods.

@InProceedings{Pedregosa2018VRTOS,
  author      = {Pedregosa, Fabian and Fatras, Kilian and Casotto, Mattia},
  title       = {Variance Reduced Three Operator Splitting},
  journal     = {?},
  year        = {2018}
}

Here is a list of my volunteering activities and the different projects I contributed to:

1. Python for Optimal Transport (POT) is an open source library for optimal transport in Python.
2. Reviewer for JMLR.