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 …

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

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 …

Concentration of tempered posteriors and of their variational approximations Page 1 The
Annals of Statistics 2020, Vol. 48, No. 3, 1475–1497 https://doi.org/10.1214/19-AOS1855 © …

Matrix completion (MC) aims at recovering missing entries, given an incomplete matrix.
Existing algorithms for MC are mainly designed for noiseless or Gaussian noise scenarios …

Supplementary material to “Estimation bounds and sharp oracle inequalities of regularized
procedures with Lipschitz loss functions”. In the supplementary material, we provide a …

Robust matrix completion refers to recovering a low-rank matrix given a subset of the entries
corrupted by gross errors, and has various applications since many real-world signals can …

We consider the properties of a specific distribution of mixed quantum states of arbitrary
dimension that can be biased towards a specific mean purity. In particular, we analyze …

This paper investigates the problem of simultaneously predicting multiple binary responses
by utilizing a shared set of covariates. Our approach incorporates machine learning …

Models with dimension more than the available sample size are now commonly used in
various applications. A sensible inference is possible using a lower-dimensional structure. In …