  This habilitation thesis presents several contributions to (1) the
PAC-Bayesian analysis of statistical learning, (2) the three aggregation
problems: given d functions, how to predict as well as (i) the best of these d
functions (model selection type aggregation), (ii) the best convex combination
of these d functions, (iii) the best linear combination of these d functions,
(3) the multi-armed bandit problems.
