We investigate the adversarial robustness of streaming algorithms. In this context, an
algorithm is considered robust if its performance guarantees hold even if the stream is …

In large scale machine learning, random sampling is a popular way to approximate datasets
by a small representative subset of examples. In particular, sensitivity sampling is an …

Given an input that undergoes a sequence of updates, a dynamic algorithm maintains a
valid solution to some predefined problem at any point in time; the goal is to design an …

The seminal work of Cohen and Peng [CP15](STOC 2015) introduced Lewis weight
sampling to the theoretical computer science community, which yields fast row sampling …

Clustering is an important technique for identifying structural information in large-scale data
analysis, where the underlying dataset may be too large to store. In many applications …

Grou** together similar elements in datasets is a common task in data mining and
machine learning. In this paper, we study streaming and parallel algorithms for correlation …

Classical streaming algorithms operate under the (not always reasonable) assumption that
the input stream is fixed in advance. Recently, there is a growing interest in designing robust …

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 streaming algorithm is considered to be adversarially robust if it provides correct outputs
with high probability even when the stream updates are chosen by an adversary who may …

A streaming algorithm is adversarially robust if it is guaranteed to perform correctly even in
the presence of an adaptive adversary. The development and analysis of such algorithms …