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 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) …

We consider the problem of estimating the mean of a sequence of random elements $ f
(\theta, X_1) $$,\ldots, $$ f (\theta, X_n) $ where $ f $ is a fixed scalar function …

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 …

We focus on a stochastic learning model where the learner observes a finite set of training
examples and the output of the learning process is a data-dependent distribution over a …

We present a new family of information-theoretic generalization bounds, in which the
training loss and the population loss are compared through a jointly convex function. This …

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 present new information-theoretic generalization guarantees through the a novel
construction of the" neighboring-hypothesis" matrix and a new family of stability notions …