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 …

Computing optimal transport distances such as the earth mover's distance is a fundamental
problem in machine learning, statistics, and computer vision. Despite the recent introduction …

In this paper, we introduce a new stochastic approximation type algorithm, namely, the
randomized stochastic gradient (RSG) method, for solving an important class of nonlinear …

Supplementary material of “Inconsistency of Bayesian Inference for Misspecified Linear
Models, and a Proposal for Repairing It”. In this paper, we described a problem for Bayesian …

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 …

This paper considers an important class of convex programming (CP) problems, namely, the
stochastic composite optimization (SCO), whose objective function is given by the …

In this paper we present a generic algorithmic framework, namely, the accelerated stochastic
approximation (AC-SA) algorithm, for solving strongly convex stochastic composite …

In this paper we study stochastic quasi-Newton methods for nonconvex stochastic
optimization, where we assume that noisy information about the gradients of the objective …

This paper studies statistical aggregation procedures in the regression setting. A motivating
factor is the existence of many different methods of estimation, leading to possibly competing …

This paper is a selective review of the regularization methods scattered in statistics literature.
We introduce a general conceptual approach to regularization and fit most existing methods …