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 …

The aim of reduced rank regression is to connect multiple response variables to multiple
predictors. This model is very popular, especially in biostatistics where multiple …

Initially designed for independent datas, low-rank matrix completion was successfully
applied in many domains to the reconstruction of partially observed high-dimensional time …

Abstract We revisit the Pseudo-Bayesian approach to the problem of estimating density
matrix in quantum state tomography in this paper. Pseudo-Bayesian inference has been …

In this paper, we study the low-rank matrix completion problem, a class of machine learning
problems, that aims at the prediction of missing entries in a partially observed matrix. Such …