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 …

We develop a framework for learning sparse nonparametric directed acyclic graphs (DAGs)
from data. Our approach is based on a recent algebraic characterization of DAGs that led to …

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

Sufficient dimension reduction is a rapidly developing research field that has wide
applications in regression diagnostics, data visualization, machine learning, genomics …

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 …

We study the problem of signal estimation from non-linear observations when the signal
belongs to a low-dimensional set buried in a high-dimensional space. A rough heuristic …

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 …

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 …

Consider measuring a vector through the inner product with several measurement vectors,. It
is common in both signal processing and statistics to assume the linear response model …