This paper reviews the state-of-the-art in denoising methods for biological microscopy
images and introduces a new and original sparsity-based algorithm. The proposed method …

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 …

Online learning is a framework for the design and analysis of algorithms that build predictive
models by processing data one at the time. Besides being computationally efficient, online …

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

Praise for the first edition:"[This book] succeeds singularly at providing a structured
introduction to this active field of research.… it is arguably the most accessible overview yet …

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 …

The purpose of these lecture notes is to provide an introduction to the general theory of
empirical risk minimization with an emphasis on excess risk bounds and oracle inequalities …

This book presents the state of the art in sparse and multiscale image and signal processing,
covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and …

We exhibit a strong link between frequentist PAC-Bayesian bounds and the Bayesian
marginal likelihood. That is, for the negative log-likelihood loss function, we show that the …