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 …

Generalised Bayesian learning algorithms are increasingly popular in machine learning,
due to their PAC generalisation properties and flexibility. The present paper aims at …

We present a unified framework for deriving PAC-Bayesian generalization bounds. Unlike
most previous literature on this topic, our bounds are anytime-valid (ie, time-uniform) …

The PAC-Bayesian approach is a powerful set of techniques to derive nonasymptotic risk
bounds for random estimators. The corresponding optimal distribution of estimators, usually …

Most PAC-Bayesian bounds hold in the batch learning setting where data is collected at
once, prior to inference or prediction. This somewhat departs from many contemporary …

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 …

Bayesian fractional posteriors Page 1 The Annals of Statistics 2019, Vol. 47, No. 1, 39–66
https://doi.org/10.1214/18-AOS1712 © Institute of Mathematical Statistics, 2019 BAYESIAN …

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 …

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 ρ ρ to its empirical …