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 …

We propose the first general PAC-Bayesian generalization bounds for adversarial
robustness, that estimate, at test time, how much a model will be invariant to imperceptible …

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 …

We propose data-dependent uniform generalization bounds by approaching the problem
from a PAC-Bayesian perspective. We first apply the PAC-Bayesian framework on “random …

We study the generalisation properties of majority voting on finite ensembles of classifiers,
proving margin-based generalisation bounds via the PAC-Bayes theory. These provide state …

Abstract The Sliced-Wasserstein distance (SW) is a computationally efficient and
theoretically grounded alternative to the Wasserstein distance. Yet, the literature on its …

PAC-Bayesian bounds are known to be tight and informative when studying the
generalization ability of randomized classifiers. However, they require a loose and costly …