Empirical risk minimization (ERM) is typically designed to perform well on the average loss,
which can result in estimators that are sensitive to outliers, generalize poorly, or treat …

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 …

Mathematical optimization has always been at the heart of engineering, statistics, and
economics. In these applied domains, optimization concepts and methods have often been …

Many contemporary applications in signal processing and machine learning give rise to
structured nonconvex nonsmooth optimization problems that can often be tackled by simple …

Most modern large-scale multi-agent systems operate by taking actions based on local data
and cooperate by exchanging information over communication networks. Due to the …

Chance-constrained programs (CCPs) constitute a difficult class of stochastic programs due
to its possible nondifferentiability and nonconvexity even with simple linear random …

This paper proposes the use of a variant of the conditional value-at-risk (CVaR) risk
measure, called the interval conditional value-at-risk (In-CVaR), for the treatment of outliers …

This paper studies the class of two-stage stochastic programs with a linearly bi-
parameterized recourse function defined by a convex quadratic program. A distinguishing …

In this paper, we introduce a three-operator splitting algorithm with deviations for solving the
minimization problem composed of the sum of two convex functions minus a convex and …

This paper studies a structured compound stochastic program (SP) involving multiple
expectations coupled by nonconvex and nonsmooth functions. We present a successive …