CLASSIC Research team

Computational Learning, Aggregation, Supervised Statistical, Inference, and Classification

Team presentation

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.

Research themes

We are involved in the following applications:
  • improving prediction through the on-line aggregation of predictors applied to
  • air quality control
  • electricity consumption
  • stock management in the retail supply chain)
  • natural image analysis, and more precisely the use of unsupervised learning in data representation
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 L1 penalization
  • interactions between unsupervised learning, information theory and adaptive data representation
  • individual sequence theory
  • multi-armed bandit problems indexed by a continuous set

International and industrial relations

  • EDF R&D (OSIRIS team) and the startup Lokad.com
  • member of the PASCAL European network of Excellence, international cooperation with Chile
  • member of the CNRS research network (GDR) on game theory
  • part of the following ANR projects: ATLAS (young researchers), EXPLO/RA (conception and simulation program), SP Bayes (general program)

Keywords: Computational Learning Aggregation Supervised Statistical Inference And Classification

Research teams of the same theme :

Inria Headquarters and research centres

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