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

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

One of the beauties of the projected gradient descent method lies in its rather simple
mechanism and yet stable behavior with inexact, stochastic gradients, which has led to its …

In this paper, we consider an online optimization process, where the objective functions are
not convex (nor concave) but instead belong to a broad class of continuous submodular …

In this paper, we study the problem of constrained and stochastic continuous submodular
maximization. Even though the objective function is not concave (nor convex) and is defined …

Submodularity is a discrete domain functional property that can be interpreted as mimicking
the role of well-known convexity/concavity properties in the continuous domain. Submodular …

We study the problem of maximizing a monotone submodular function subject to a
cardinality constraint $ k $, with the added twist that a number of items $\tau $ from the …

In this paper we study the fundamental problems of maximizing a continuous nonmonotone
submodular function over the hypercube, both with and without coordinate-wise concavity …

We study the classical problem of maximizing a monotone submodular function subject to a
cardinality constraint k, with two additional twists:(i) elements arrive in a streaming fashion …

While marketing budget allocation has been studied for decades in traditional business,
nowadays online business brings much more challenges due to the dynamic environment …