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

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 …

We introduce a novel framework of Prophet Inequalities for combinatorial valuation
functions. For a (n on-monotone) submodular objective function over an arbitrary matroid …

Online optimization has been a successful framework for solving large-scale problems
under computational constraints and partial information. Current methods for online convex …

We investigate the problem of stochastic, combinatorial multi-armed bandits where the
learner only has access to bandit feedback and the reward function can be non-linear. We …

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 …

In this paper, we propose three online algorithms for submodular maximization. The first
one, Mono-Frank-Wolfe, reduces the number of per-function gradient evaluations from …

Privacy restrictions imposed by browsers such as Safari and Chrome limit the quality of
individual-level data used in personalization algorithms. This paper investigates the …

This paper studies the problem of cHarging tAsk Scheduling for direcTional wireless chargEr
networks (HASTE), ie, given a set of rotatable directional wireless chargers on a 2D area …

In multi-agent coverage control problems, agents navigate their environment to reach
locations that maximize the coverage of some density. In practice, the density is rarely …