Most papers below can also be downloaded from my Google Scholar profile.

Of course the classification below is a little arbitrary because statistical concepts are ubiquitous in online learning theory, and online algorithms can also be used in the classical statistical (batch i.i.d.) setting.


Online learning theory

  • Uniform regret bounds over R^d for the sequential linear regression problem with the square loss.   [proceedings]
    Pierre Gaillard, Sébastien Gerchinovitz, Malo Huard, and Gilles Stoltz.
    Proceedings of the 30th international conference on Algorithmic Learning Theory (ALT 2019). In press.
  • Algorithmic chaining and the role of partial feedback in online nonparametric learning.
    Nicolò Cesa-Bianchi, Pierre Gaillard, Claudio Gentile, and Sébastien Gerchinovitz. [long version]
    Proceedings of the 2017 Conference on Learning Theory, PMLR 65:465-481, 2017. [short version]
  • Refined lower bounds for adversarial bandits.   [proceedings] [pdf]
    Sébastien Gerchinovitz and Tor Lattimore.
    Advances in Neural Information Processing Systems 29 (NIPS 2016), pp. 1198-1206, 2016.
  • A chaining algorithm for online nonparametric regression.   [proceedings] [pdf] [video]
    Pierre Gaillard and Sébastien Gerchinovitz.
    Proceedings of the 28th Conference on Learning Theory (COLT 2015), pp. 764–796, 2015.
  • Adaptive and optimal online linear regression on L1-balls.   [journal] [pdf]
    Sébastien Gerchinovitz and Jia Yuan Yu.
    Theoretical Computer Science 519:4-28, 2014.
    NB : A shorter version appeared in the proceedings of ALT 2011.
  • Sparsity regret bounds for individual sequences in online linear regression.   [journal] [pdf]
    Sébastien Gerchinovitz.
    Journal of Machine Learning Research 14(Mar):729-769, 2013.
    NB : A shorter version appeared in the proceedings of COLT 2011. Here is a video of the talk.

Statistical theory

  • Optimal functional supervised classification with separation condition.   [preprint]
    Sébastien Gadat, Sébastien Gerchinovitz, and Clément Marteau.
    arXiv:1801.03345, 2018.
  • Fano's inequality for random variables.   [preprint]
    Sébastien Gerchinovitz, Pierre Ménard, and Gilles Stoltz.
    arXiv:1702.05985, 2017.

Real-world applications

  • Optimization of a SSP's Header Bidding Strategy using Thompson Sampling.   [pdf] [video]
    Grégoire Jauvion, Nicolas Grislain, Pascal Dkengne Sielenou, Aurélien Garivier, and Sébastien Gerchinovitz.
    Proceedings of KDD 2018, Applied Data Science track, 2018.
  • Adaptive simulated annealing with homogenization for aircraft trajectory optimization.   [proceedings]
    Clément Bouttier, Olivier Babando, Sébastien Gadat, Sébastien Gerchinovitz, Serge Laporte, and Florence Nicol.
    Operations Research Proceedings 2015.
  • A multiple-play bandit algorithm applied to recommender systems.   [proceedings] [pdf]
    Jonathan Louëdec, Max Chevalier, Josiane Mothe, Aurélien Garivier, and Sébastien Gerchinovitz.
    Proceedings of the 28th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2015), pp. 67-72, 2015.

PhD thesis

Technical reports

Last update: March 2019