This paper considers stochastic optimization problems for a large class of objective
functions, including convex and continuous submodular. Stochastic proximal gradient …

It is generally believed that submodular functions–and the more general class of $\gamma $-
weakly submodular functions–may only be optimized under the non-negativity assumption …

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 …

DR-submodular continuous functions are important objectives with wide real-world
applications spanning MAP inference in determinantal point processes (DPPs), and mean …

Randomization is a fundamental tool used in many theoretical and practical areas of
computer science. We study here the role of randomization in the area of submodular …

We present a general technique for the analysis of first-order methods. The technique relies
on the construction of a duality gap for an appropriate approximation of the objective …

Submodular maximization under various constraints is a fundamental problem studied
continuously, in both computer science and operations research, since the late 1970's. A …

It is known that greedy methods perform well for maximizing\textitmonotone submodular
functions. At the same time, such methods perform poorly in the face of non-monotonicity. In …

The study of combinatorial optimization problems with submodular objectives has attracted
much attention in recent years. Such problems are important in both theory and practice …

In this paper, we develop the first one-pass streaming algorithm for submodular
maximization that does not evaluate the entire stream even once. By carefully subsampling …