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 …

Sleep disorders affect a large portion of the global population and are strong predictors of
morbidity and all-cause mortality. Sleep staging segments a period of sleep into a sequence …

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

Virtually any model we use in machine learning to make predictions does not perfectly
represent reality. So, most of the learning happens under model misspecification. In this …

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 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 …

Ensembles are widely used in machine learning and, usually, provide state-of-the-art
performance in many prediction tasks. From the very beginning, the diversity of an ensemble …

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 …

Ensembles of machine learning models have been well established as a powerful method of
improving performance over a single model. Traditionally, ensembling algorithms train their …

In this paper, we investigate the question: _Given a small number of datapoints, for example
$ N= 30$, how tight can PAC-Bayes and test set bounds be made? _ For such small …