  We study the problem of aggregation under the squared loss in the model of
regression with deterministic design. We obtain sharp PAC-Bayesian risk bounds
for aggregates defined via exponential weights, under general assumptions on
the distribution of errors and on the functions to aggregate. We then apply
these results to derive sparsity oracle inequalities.
