Kilian Fatras

PhD Student
IRISA-INRIA Rennes & Obelix


******I am looking for a PostDoc !******


Welcome to my personal website !

I am a final year 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 with applications in large scale settings and noisy labels.

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.


News !


  1. I am looking for a PostDoc ! Do not hesitate to reach me if you have opportunities in your lab !

  2. 3 new papers out :
  3. POT: Python Optimal Transport has been accepted to JMLR Open Source Software !
  4. Unbalanced minibatch Optimal Transport; applications to Domain Adaptation.
  5. Minibatch Optimal Transport distances; analysis and applications

Research interests


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


Papers


Optimal Transport

POT: Python Optimal Transport
Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Alexander Tong and Titouan Vayer
Journal of Machine Learning Research (JMLR) - Open Source Software, 2021

Paper Website Code bibtex
  @article{JMLR:v22:20-451,
    author  = {R\'emi Flamary and Nicolas Courty and Alexandre Gramfort and Mokhtar Z. Alaya and Aur\'elie Boisbunon and Stanislas Chambon and Laetitia Chapel and Adrien Corenflos and Kilian Fatras and Nemo Fournier and L\'eo Gautheron and Nathalie T.H. Gayraud and Hicham Janati and Alain Rakotomamonjy and Ievgen Redko and Antoine Rolet and Antony Schutz and Vivien Seguy and Danica J. Sutherland and Romain Tavenard and Alexander Tong and Titouan Vayer},
    title   = {POT: Python Optimal Transport},
    journal = {Journal of Machine Learning Research},
    year    = {2021},
    volume  = {22},
    number  = {78},
    pages   = {1-8},
    url     = {http://jmlr.org/papers/v22/20-451.html}
  }

Unbalanced minibatch Optimal Transport; applications to Domain Adaptation
Kilian Fatras, Thibault Séjourné, Nicolas Courty and Rémi Flamary
Preprint, 2021

Paper bibtex
  @misc{fatras2021unbalanced,
    title={Unbalanced minibatch Optimal Transport; applications to Domain Adaptation}, 
    author={Kilian Fatras and Thibault Séjourné and Nicolas Courty and Rémi Flamary},
    year={2021},
    eprint={2103.03606},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

Minibatch Optimal Transport distances; analysis and applications
Kilian Fatras, Younes Zine, Szymon Majewski, Rémi Flamary, Rémi Gribonval and Nicolas Courty
Preprint, 2021

Paper Code bibtex
  @misc{fatras2021minibatch,
    title={Minibatch optimal transport distances; analysis and applications}, 
    author={Kilian Fatras and Younes Zine and Szymon Majewski and Rémi Flamary and Rémi Gribonval and Nicolas Courty},
    year={2021},
    eprint={2101.01792},
    archivePrefix={arXiv},
    primaryClass={stat.ML}
}

Generating natural adversarial Remote Sensing Images
Jean-Christophe Burnel, Kilian Fatras, Rémi Flamary and Nicolas Courty
Preprint, 2020

Paper Code bibtex
@unpublished{burnelARGAN,
  TITLE = {{Generating natural adversarial Remote Sensing Images}},
  AUTHOR = {Burnel, Jean-Christophe and Fatras, Kilian and Flamary, R{\'e}mi and Courty, Nicolas},
  URL = {https://hal.archives-ouvertes.fr/hal-02558542},
  NOTE = {working paper or preprint},
  YEAR = {2020},
  MONTH = Apr,
  KEYWORDS = {Deep Learning ; Remote sensing ; Generative models ; Adversarial Examples},
  PDF = {https://hal.archives-ouvertes.fr/hal-02558542/file/ARGAN_TGRS_hal.pdf},
  HAL_ID = {hal-02558542},
}

Learning with minibatch Wasserstein: asymptotic and gradient properties
Kilian Fatras, Younes Zine, Rémi Flamary, Rémi Gribonval and Nicolas Courty
Proceedings of the 23nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2020

Paper Code Slides Poster Blog bibtex
@InProceedings{pmlr-v108-fatras20a,
  title = 	 {Learning with minibatch Wasserstein  : asymptotic and gradient properties},
  author = 	 {Fatras, Kilian and Zine, Younes and Flamary, R\'emi and Gribonval, Remi and Courty, Nicolas},
  booktitle = 	 {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics},
  pages = 	 {2131--2141},
  year = 	 {2020},
  editor = 	 {Chiappa, Silvia and Calandra, Roberto},
  volume = 	 {108},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Online},
  month = 	 {26--28 Aug},
  publisher = 	 {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v108/fatras20a/fatras20a.pdf},
  url = 	 {http://proceedings.mlr.press/v108/fatras20a.html},
  abstract = 	 {Optimal transport distances are powerful tools to compare probability distributions and have found many applications in machine learning. Yet their algorithmic complexity prevents their direct use on large scale datasets. To overcome this challenge, practitioners compute these distances on minibatches i.e., they average the outcome of several smaller optimal transport problems. We propose in this paper an analysis of this practice, which effects are not well understood so far. We notably argue that it is equivalent to an implicit regularization of the original problem, with appealing properties such as unbiased estimators, gradients and a concentration bound around the expectation, but also with defects such as loss of distance property. Along with this theoretical analysis, we also conduct empirical experiments on gradient flows, GANs or color transfer that highlight the practical interest of this strategy.}
}

Wasserstein Adversarial Regularization (WAR) on label noise
Kilian Fatras*, Bharath Damodaran*, Sylvain Lobry, Rémi Flamary, Devis Tuia and Nicolas Courty
* equal contribution
Preprint, 2020

Paper bibtex
@article{damodaran2019war,
author = {Bhushan Damodaran, Bharath and Fatras, Kilian and Lobry, Sylvain and Flamary, Rémi and Tuia, Devis and Courty, Nicolas},
title  = {Wasserstein Adversarial Regularization (WAR) on label noise},
year   = {2019 (Submited)}
}

Optimization

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

Paper bibtex
@InProceedings{Pedregosa2019PSVR,
      author      = {Pedregosa, Fabian and Fatras, Kilian and Casotto, Mattia},
      title       = {Proximal Splitting Meets Variance Reduction},
      booktitle   = {AISTATS},
      year        = {2019}
                  }


Workshops


This section is empty for the moment !

Projects and Volunteering


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

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

Contacts


I use several networks, do not hesitate to reach me out !