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 …

This paper considers a class of constrained stochastic composite optimization problems
whose objective function is given by the summation of a differentiable (possibly nonconvex) …

Nonsmooth nonconvex optimization problems broadly emerge in machine learning and
business decision making, whereas two core challenges impede the development of …

We consider regularized stochastic learning and online optimization problems, where the
objective function is the sum of two convex terms: one is the loss function of the learning …

The past decade has seen the rapid growth of model based image reconstruction (MBIR)
algorithms, which are often applications or adaptations of convex optimization algorithms …

In this paper, we study the nonnegative matrix factorization problem under the separability
assumption (that is, there exists a cone spanned by a small subset of the columns of the …

This work studies applications and generalizations of a simple estimation technique that
provides exponential concentration under heavy-tailed distributions, assuming only …

In this paper, we present a new conditional gradient type method for convex optimization by
calling a linear optimization (LO) oracle to minimize a series of linear functions over the …

We study differentially private stochastic optimization in convex and non-convex settings. For
the convex case, we focus on the family of non-smooth generalized linear losses (GLLs) …

Gradient-free/zeroth-order methods for black-box convex optimization have been
extensively studied in the last decade with the main focus on oracle calls complexity. In this …