Combining PAC-Bayesian and Generic Chaining Bounds

There exist many different generalization error bounds in statistical learning theory. Each of these bounds contains an improvement over the others for certain situations or algorithms. Our goal is, first, to underline the links between these bounds, ...

There exist many different generalization error bounds for classification. Each of these bounds contains an improvement over the others for certain situations. Our goal is to combine these different improvements into a single bound. In particular we ...

PAC-Bayesian learning bounds are of the utmost interest to the learning community. Their role is to connect the generalization ability of an aggregation distribution $$\rho $$? to its empirical risk and to its Kullback-Leibler divergence with respect to ...