Modern signal processing (SP) methods rely very heavily on probability and statistics to
solve challenging SP problems. SP methods are now expected to deal with ever more …

This paper provides a review and commentary on the past, present, and future of numerical
optimization algorithms in the context of machine learning applications. Through case …

We study the extension of the proximal gradient algorithm where only a stochastic gradient
estimate is available and a relaxation step is allowed. We establish convergence rates for …

Accelerated algorithms have broad applications in large-scale optimization, due to their
generality and fast convergence. However, their stability in the practical setting of noise …

This paper is concerned with the convergence of over-relaxations of the forward-backward
algorithm (FB)(in particular the fast iterative soft thresholding algorithm (FISTA)) in the case …

The purpose of this paper is to establish the almost sure weak ergodic convergence of a
sequence of iterates (x_n) given by x_n+1=(I+\lambda_nA(n+1,\,.\,))^-1(x_n), where …

We study a risk-constrained version of the stochastic shortest path (SSP) problem, where the
risk measure considered is Conditional Value-at-Risk (CVaR). We propose two algorithms …

Gaussian processes provide a framework for nonlinear nonparametric Bayesian inference
widely applicable across science and engineering. Unfortunately, their computational …

Stochastic approximation techniques play an important role in solving many problems
encountered in machine learning or adaptive signal processing. In these contexts, the …

We propose and analyze the convergence of a novel stochastic algorithm for monotone
inclusions that are sum of a maximal monotone operator and a single-valued cocoercive …