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 …

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 …

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 …

A growing body of research has shown that many classifiers are susceptible to adversarial
examples–small strategic modifications to test inputs that lead to misclassification. In this …

In the online learning with experts problem, an algorithm must make a prediction about an
outcome on each of $ T $ days (or times), given a set of $ n $ experts who make predictions …

Maximizing a monotone submodular function is a fundamental task in machine learning. In
this paper we study the deletion robust version of the problem under the classic matroids …

Machine learning-based forecasting models are commonly used in Intelligent Transportation
Systems (ITS) to predict traffic patterns and provide city-wide services. However, most of the …

The problem of selecting a small-size representative summary of a large dataset is a
cornerstone of machine learning, optimization and data science. Motivated by applications …

In the online learning with experts problem, an algorithm makes predictions about an
outcome on each of $ T $ days, given a set of $ n $ experts who make predictions on each …