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:
regression models used for supervised learning, from different
perspectives: the PAC-Bayesian approach to generalization bounds; robust estimators;
model selection and model aggregation;
sparse models of prediction and –regularization;
interactions between unsupervised learning, information theory and
adaptive data representation;
individual sequence theory;
multi-armed bandit problems (possibly indexed by a continuous set).
We are involved in the following applications:
the improvement of prediction through the on-line aggregation of predictors, with an emphasis on the forecasting of
air quality, electricity consumption, production data of oil reservoirs;
natural image analysis, and more precisely the use of unsupervised
learning in data representation;
statistical inference on biological and neurobiological data.