International audienceIn the same spirit as Tsybakov (2003), we define the optimality of an aggregation procedure in the problem of classification. Using an aggregate with exponential weights, we obtain an optimal rate of convex aggregation for the hinge risk under the margin assumption. Moreover we obtain an optimal rate of model selection aggregation under the margin assumption for the excess Bayes risk
This paper studies statistical aggregation procedures in the regression setting. A motivating factor...
Abstract: Given a finite collection of estimators or classifiers, we study the problem of model sele...
Given a finite collection of estimators or classifiers, we study the problem of model selection type...
15 pagesLet $\cF$ be a set of $M$ classification procedures with values in $[-1,1]$. Given a loss fu...
Given a finite class of functions F, the problem of aggregation is to construct a procedure with a r...
Given a finite set F of estimators, the problem of aggregation is to construct a new estimator whose...
We study the performances of the empirical risk minimization procedure (ERM for short), with respect...
In this paper we prove the optimality of an aggregation procedure. We prove lower bounds for aggrega...
In this thesis we deal with aggregationprocedures under the margin assumption. We prove that the mar...
Proceedings of the 16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, ...
accepted to COLT 2006We consider the problem of optimality, in a minimax sense, and adaptivity to th...
It is generally believed that ensemble approaches, which combine multiple algorithms or models, can ...
We introduce an alternative to the notion of ‘fast rate’ in Learning Theory, which coincides with th...
We study the performance of empirical risk minimization (ERM), with respect to the quadratic risk, i...
International audienceLet $F$ be a finite model of cardinality $M$ and denote by $\conv(F)$ its conv...
This paper studies statistical aggregation procedures in the regression setting. A motivating factor...
Abstract: Given a finite collection of estimators or classifiers, we study the problem of model sele...
Given a finite collection of estimators or classifiers, we study the problem of model selection type...
15 pagesLet $\cF$ be a set of $M$ classification procedures with values in $[-1,1]$. Given a loss fu...
Given a finite class of functions F, the problem of aggregation is to construct a procedure with a r...
Given a finite set F of estimators, the problem of aggregation is to construct a new estimator whose...
We study the performances of the empirical risk minimization procedure (ERM for short), with respect...
In this paper we prove the optimality of an aggregation procedure. We prove lower bounds for aggrega...
In this thesis we deal with aggregationprocedures under the margin assumption. We prove that the mar...
Proceedings of the 16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, ...
accepted to COLT 2006We consider the problem of optimality, in a minimax sense, and adaptivity to th...
It is generally believed that ensemble approaches, which combine multiple algorithms or models, can ...
We introduce an alternative to the notion of ‘fast rate’ in Learning Theory, which coincides with th...
We study the performance of empirical risk minimization (ERM), with respect to the quadratic risk, i...
International audienceLet $F$ be a finite model of cardinality $M$ and denote by $\conv(F)$ its conv...
This paper studies statistical aggregation procedures in the regression setting. A motivating factor...
Abstract: Given a finite collection of estimators or classifiers, we study the problem of model sele...
Given a finite collection of estimators or classifiers, we study the problem of model selection type...