Many machine learning tasks involve solving complex optimization problems, such as
working on non-differentiable, non-continuous, and non-unique objective functions; in some …

In this paper we study the adaptive complexity of submodular optimization. Informally, the
adaptive complexity of a problem is the minimal number of sequential rounds required to …

We study the problem of maximizing a function that is approximately submodular under a
cardinality constraint. Approximate submodularity implicitly appears in a wide range of …

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 …

In this paper we study the adaptivity of submodular maximization. Adaptivity quantifies the
number of sequential rounds that an algorithm makes when function evaluations can be …

The problem of selecting the best $ k $-element subset from a universe is involved in many
applications. While previous studies assumed a noise-free environment or a noisy …

Stochastic optimization of continuous objectives is at the heart of modern machine learning.
However, many important problems are of discrete nature and often involve submodular …

Subset selection, aiming to select the best subset from a ground set with respect to some
objective function, is a fundamental problem with applications in many areas, such as …

We consider the problem of maximizing a monotone submodular function under noise.
Since the 1970s there has been a great deal of work on optimization of submodular …

Submodular maximization has wide applications in machine learning and data mining,
where massive datasets have brought the great need for designing efficient and …