Stéphane Boucheron
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Stéphane Boucheron
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Concentration inequalities in the infinite urn scheme foroccupancy counts and the missing mass, with applications
Discussion of ``Concentration for (regularized) empiricalrisk minimization'' by Sara van de Geer and MartinWainwright [ MR3707417]
Concentration inequalities. A nonasymptotic theory of independence. Corrected paperback edition.
About adaptive coding on countable alphabets: max-stableenvelope classes
Tail index estimation, concentration and adaptivity
About adaptive coding on countable alphabets
Concentration inequalities
Adaptive compression against a countable alphabet
Concentration inequalities for order statistics
Sharp threshold for percolation on expanders
A high-dimensional Wilks phenomenon
Coding on countably infinite alphabets
On concentration of self-bounding functions
Error exponents for AR order testing
Moment inequalities for functions of independent randomvariables
Theory of classification: a survey of some recent advances
Concentration Inequalities
Concentration inequalities using the entropy method
Introduction to Statistical Learning Theory
Optimal error exponents in hidden Markov models orderestimation
Problèmes informatiques et techniques probabilistes
Bins and balls: large deviations of the empirical occupancyprocess
Model Selection and Error Estimation
Problèmes d'informatique fondamentale. Voyages au pays de l'informatique fondamentale au gré de problm̀es de concours.
A sharp concentration inequality with applications
About priority encoding transmission
Model Selection and Error Estimation
An urn model from learning theory
Noise Injection: Theoretical Prospects
De la représentation à la validation~: lagénéralisation dans les réseaux de neurones
Control of complexity in learning with perturbed inputs
Sur les traces de l'apprentissage
Algorithmique parallèle
Théorie de l'apprentissage, de l'appoche formele aux enjeux cognitifs
Some remarks about space-complexity of learning, & circuitcomplexity of recognizing
Bruit et apprentissage
Apprentissage et calculs
Learnability in the Presence of Noise
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