Aggregated predictors are obtained by making a set of basic predictors vote according to
some weights, that is, to some probability distribution. Randomized predictors are obtained …

This paper presents an empirical study regarding training probabilistic neural networks
using training objectives derived from PAC-Bayes bounds. In the context of probabilistic …

While PAC-Bayes is now an established learning framework for light-tailed losses (\emph
{eg}, subgaussian or subexponential), its extension to the case of heavy-tailed losses …

A fundamental question in theoretical machine learning is generalization. Over the past
decades, the PAC-Bayesian approach has been established as a flexible framework to …

We propose novel statistics which maximise the power of a two-sample test based on the
Maximum Mean Discrepancy (MMD), byadapting over the set of kernels used in defining it …

Minimising upper bounds on the population risk or the generalisation gap has been widely
used in structural risk minimisation (SRM)--this is in particular at the core of PAC-Bayesian …

We focus on a specific class of shallow neural networks with a single hidden layer, namely
those with $ L_2 $-normalised data and either a sigmoid-shaped Gaussian error function …

Despite its wide use and empirical successes, the theoretical understanding and study of the
behaviour and performance of the variational autoencoder (VAE) have only emerged in the …

We give a general recipe for derandomising PAC-Bayesian bounds using margins, with the
critical ingredient being that our randomised predictions concentrate around some value …

We introduce a modified version of the excess risk, which can be used to obtain empirically
tighter, faster-rate PAC-Bayesian generalisation bounds. This modified excess risk …