Page personnelle en français (ce n'est pas une traduction de ma page anglaise, mais plutôt un complément portant sur des informations plus spécifiquement francophones.)
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Olivier Catoni
Directeur de Recherche
CREST, Laboratoire de Statistiques
UMR 9194 du CNRS
bureau 3035
5, avenue Henry Le Chatelier
TSA 96642
91764 Palaiseau cedex,
FRANCE
e-mail: olivier.catoni
followed by « at » ensae.fr
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Three-day meeting of statisticians in Paris
I.H.P., July 18-20 2022
Another talk
on k-means with updated results.
My research report (in French)
Conference on robustness and privacy 2021
You can download the slides
of my talk on generalization
bounds for means and k-means.
New bounds for k-means algorithms
This is a joint work with Gautier Appert.
The preprint is here.
Gautier's PhD is
here.
About stochastic games
Olivier Catoni, Miquel Oliu-Barton et Bruno Ziliotto. « Constant
payoff in zero-sum stochastic games ». In : Ann. Inst. H. Poincaré
Probab. Statist. (2020). to appear, p. 1-13 pdf
Conférence en l'honneur de Robert Azencott, May 14-15 2019
I was very pleased to pay a tribute to my former PhD adviser Robert Azencott
in this conference dedicated to his impact on Artificial Intelligence.
I gave a talk about statistical syntax analysis for signal processing.
NIPS 2017 Workshop, Long Beach, CA, USA, December 9, 2017
I gave two talks, one as invited speaker
on dimension-free PAC-Bayesian bounds for vectors and matrices and another more specific one
on a srinkage estimator of the mean of a random vector, in the workshop
(Almost) 50 Shades of Bayesian Learning: PAC-Bayesian trends and insights, part of NIPS 2017. I presented the two joint works with Ilaria Giulini that you can download
below.
Dimension-free PAC-Bayesian bounds, joint work with Ilaria Giulini,
December 2017
Two new papers are available:
Dimension-free PAC-Bayesian bounds for matrices.
vectors and linear least squares regression
(You may experience some font problems, depending on your
pdf viewer.)
Dimension-free PAC-Bayesian bounds for
the estimation of the mean of a random vector
NIPS 2017 workshop; (Almost) 50 shades of Bayesian learning: PAC-Bayesian trends and insights.
Markov substitute processes, June 2017
I gave a talk at INRIA Lille,
on the subject of Markov substitute processes.
This was focussed mainly on two results: Markov substitute processes form
exponential families (or Gibbs measures in other terms), and crossing-over
dynamics can be used to compute the maximum likelihood estimator.
PAC Bayesian bounds for the Gram matrix and least squares regression
with a random design
I posted a paper on the
subject on ArXiv. I also gave a talk
at the CREST seminar in January 2016, and an earlier one
in May 2015 at the SMILE seminar at ENS.
A talk on spectral clustering
Given at the Séminaire Parisien de Statistiques, in October 2015.
Two video talks on PAC-Bayes learning bounds at the IFCAM Summer School on Applied Mathematics,
on July 2014
Slides with sound :
An informal introduction to PAC-Bayes bounds (with a small mistake in the definition of psi, that should be equal to the log of what it is claimed to be)
PAC-Bayes Bounds for arbitrary loss functions
Bounds for binary loss functions
PAC-Bayes bounds for binary loss functions
PAC-Bayes margin bounds for Support Vector Mahines
A small test catoni_videotest.avi
(small video file to test if you can read the format I am using).
Slides in pdf format without sound
Lecture notes
Markov substitute models and statistical inference in linguistics
A talk , given in April 2014 at the
Séminaire Parisien de Statistique.
A talk at IST Austria
PAC-Bayes bounds using Gaussian posterior distributions
Dimension dependent and dimension free PAC-Bayes bounds for the Gram matrix
Two talks in Toulouse (March 26, 2013)
Toric grammars, a new stochastic model
The statistics of Principal Component Analysis
Statistical learning of syntactic structures
Toric Grammars : a new statistical
approach to natural language modeling, Olivier Catoni and Thomas Mainguy (2013) arXiv
The simulation shown in this paper was made with the following code.
Please note that this is only a demonstration code,
suitable to show how the method behaves on small examples, but not
optimized to scale properly with large data sets.
A talk in Moscow, at the Institute for Information Transmission Problems (Nov 29, 2012)
Unsupervised statistical learning through label aggregation
,
slides.
A talk in Nice (on May 12, 2011, in French)
Petites perturbations des estimateurs et bornes PAC-Bayésiennes
Some lecture notes on PAC-Bayes bounds (Statistical learning, L3, ENS)
notes of 06/12/2013
notes of 04/02/2012
notes of 09/15/2011
My talk at ENS on March 16, 2011 (in French)
Apprentissage PAC-Bayésien : de la classification à la régression
My talk in Lille on January 21, 2011
La moyenne empirique est-elle perfectible ?
My last preprints are on arXiv (and HAL)
Challenging the empirical mean and empirical variance: a deviation study,
Olivier Catoni (2010), on arXiv
High confidence estimates of the mean of heavy-tailed real random variables,
Olivier Catoni (2009), on arXiv .
This one can be skipped : it is an early draft of the previous preprint, which presents improved
estimators, improved bounds and some experiments.
Robust linear least squares regression, Jean-Yves Audibert, Olivier Catoni
(2010), on arXiv
Robust linear regression through PAC-Bayesian truncation, Jean-Yves Audibert,
Olivier Catoni (2010), on arXiv
Risk bounds in linear regression through PAC-Bayesian truncation,
Jean-Yves Audibert, Olivier Catoni (2009), on
arXiv.
This one can be skipped also: it is an early draft covering the
matter of the two previous preprints.
My talk in video.
Exposé devant le Comité des Projets de l'INRIA ---
4 juin 2009
The slides of my talk to present the CLASSIC INRIA team proposal.
Evaluation du DMA --- 29 janvier 2009
The slides of my talk on the occasion of the evaluation of the
DMA.
Journée de rentrée du DMA --- 2 octobre 2008
You can download the slides of
my presentation.
Univ. Rennes 1, June 18-20 2007. The slides of my talk, ``Learning,
information theory and thermodynamics'', pdf file.
PAC-Bayesian supervised classification (The thermodynamics of
statistical learning)
This is the title of a monograph published in the Lecture Notes
series of the IMS. pdf file,
dvi file.
Publication list
publications
CV and report (in French)
Preprints
I moved in October 1998 from ENS to Paris 6 and back to ENS in september 2008.
My older preprints and those
of some of my students
(Cécile Cot, Gilles Blanchard and Jean-Philippe Vert
who stayed at the ENS) can be found on the
preprint server of the
Laboratoire de Mathématiques de l'Ecole Normale Supérieure
de Paris.
Preprints from the period october 1998 - september 2008 are on the
server of the laboratoire de Probabilités et
Modèles Aléatoires., on the server
HAL or on ArXiv
You can also use the
national preprint search engine
of the cellule mathdoc.
The
last revision of ``The loop erased exit path and the metastability of
a biased vote process'', a joint paper with
Dayue Chen and Jun Xie, to appear in
Stochastic Processes and their Applications, is
also available.
Click here
for the last revision of ``Free energy estimates and deviation inequalities'',
with a more precise study of the unbounded case and improved bounds for Markov
chains.
The last revision of Gibbs estimators describes
general integrability conditions under which it is possible to define a Gibbs
estimator and to bound its risk.
Lecture notes
You can download the last revision of my
lecture notes
on ``Simulated Annealing Algorithms and Markov chains with Rare
Transitions'', published in the Séminaire de Probabilités.
You can download here the draft of my Saint-Flour
lecture notes (July 2001) on statiscial learning theory and stochastic
optimization. The final version of these notes is now published
as
Springer Lecture Notes in Mathematics Number 1851.
Please consider buying the book or encouraging your
library to buy it if you liked the draft !
(as a courtesy to Springer's efforts to make the Saint Flour
summer school notes widely available: authors don't get royalties
on lecture notes, this is why I feel free to give you this piece
of advice).
Information theory, statistical learning and pattern recognition
A workshop on this theme was held at the CIRM in December 1998.
The program of this meeting is kept here.
The Gibbs estimator in action : a downloadable software for density estimation
I wrote a software to illustrate a communication I presented
to Foundations of Computational Mathematics (July 13-17 2000, Hong-Kong).
You can have a look at its documentation here,
where you will also find download instructions.
The slides of my talk.
Empirical complexity and randomized estimators
You can download here the slides (as a .dvi or
.pdf file) of
my talk at the workshop
« Statistical Learning in Classification and Model Selection »
EURANDOM, Eindhoven, The Netherlands
January 15-18, 2003, organized by
Prof.dr. R.D.Gill (Universiteit Utrecht/EURANDOM), Dr. P. Grünwald (CWI), Prof.dr A.W. van der Vaart (Vrije Universiteit Amsterdam/EURANDOM),
Dr. J. Lember (EURANDOM)
as well as the corresponding preprint (as a .dvi or
.pdf file),
(to be also available soon on the PMA server).
DEA lectures : Classification and model selection (2003)
Lecture notes in postscript and
pdf formats are available.
Théorèmes PAC Bayésiens locaux et
estimateurs randomisés
Those who understand french can download the slides of this
talk from my french homepage.
Back to the department homepage.