Section: Overall Objectives

Overall Objectives

We are a research team on machine learning, with an emphasis on statistical methods. Processing huge amounts of complex data has created a need for statistical methods which could remain valid under very weak hypotheses, in very high dimensional spaces. Our aim is to contribute to a robust, adaptive, computationally efficient and desirably non-asymptotic theory of statistics which could be profitable to learning.

Our theoretical studies bear on the following mathematical tools:

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  regression models used for supervised learning, from different perspectives: the PAC-Bayesian approach to generalization bounds; robust estimators; model selection and model aggregation;

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  sparse models of prediction and ${\ell }_1$–regularization;

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  interactions between unsupervised learning, information theory and adaptive data representation;

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  individual sequence theory;

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  multi-armed bandit problems (possibly indexed by a continuous set).

We are involved in the following applications:

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  improving prediction through the on-line aggregation of predictors applied to air quality control, electricity consumption, stock management in the retail supply chain;

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  natural image analysis, and more precisely the use of unsupervised learning in data representation;

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  computational linguistics;

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  statistical inference on biological data.