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 …

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 …

Abstract Since their inception, Variational Autoencoders (VAEs) have become central in
machine learning. Despite their widespread use, numerous questions regarding their …

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 …

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 extend PAC-Bayesian theory to generative models and develop generalization bounds
for models based on the Wasserstein distance and the total variation distance. Our first result …

We present improved algorithms with worst-case regret guarantees for the stochastic linear
bandit problem. The widely used" optimism in the face of uncertainty" principle reduces a …

This paper contains a recipe for deriving new PAC-Bayes generalisation bounds based on
the $(f,\Gamma) $-divergence, and, in addition, presents PAC-Bayes generalisation bounds …

Modern machine learning usually involves predictors in the overparametrised setting
(number of trained parameters greater than dataset size), and their training yield not only …