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Equipe SequeL

Sequential Learning

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The aim of SequeL is to study the resolution of sequential decision problems. For that purpose, we study sequential learning algorithms. We put an emphasis on the use of concepts and tools drawn from statistical learning, namely kernel methods, and Bayesian estimation methods. We favor non parametric approaches. Our work spans from theory of learnability, to the design of efficient algorithm, to applications.

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