Kilian Fatras

  @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}
  }

  @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}
}

  @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}
}

@unpublished{burnelARGAN,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},
}

@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.}
}

@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)}
}

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