In this paper, we consider the problem of Gaussian process (GP) optimization with an added
robustness requirement: The returned point may be perturbed by an adversary, and we …

Maximizing monotone submodular functions under a matroid constraint is a classic
algorithmic problem with multiple applications in data mining and machine learning. We …

In the adversarially robust streaming model, a stream of elements is presented to an
algorithm and is allowed to depend on the output of the algorithm at earlier times during the …

We consider the classical problem of maximizing a monotone submodular function subject
to a cardinality constraint, which, due to its numerous applications, has recently been …

Robustness against adversarial attacks has recently been at the forefront of algorithmic
design for machine learning tasks. In the adversarial streaming model, an adversary gives …

We consider a robust formulation, introduced by Krause et al.(J Artif Intell Res 42: 427–486,
2011), of the classical cardinality constrained monotone submodular function maximization …

Fueled by algorithmic advances, AI algorithms are increasingly being deployed in settings
subject to unanticipated challenges with complex social effects. Motivated by real-world …

There has been a flurry of recent literature studying streaming algorithms for which the input
stream is chosen adaptively by a black-box adversary who observes the output of the …

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 …

Despite the rich existing literature about minimax optimization in continuous settings, only
very partial results of this kind have been obtained for combinatorial settings. In this paper …