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 …

Laws of large numbers guarantee that given a large enough sample from some population,
the measure of any fixed sub-population is well-estimated by its frequency in the sample. We …

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 …

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 …

In fully dynamic clustering problems, a clustering of a given data set in a metric space must
be maintained while it is modified through insertions and deletions of individual points. In …

The last decade saw impressive progress towards understanding the performance of
algorithms in adaptive settings, where subsequent inputs may depend on the output from …

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 …

Streaming algorithms are algorithms for processing large data streams, using only a limited
amount of memory. Classical streaming algorithms typically work under the assumption that …

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 …

Graph coloring is a fundamental problem with wide reaching applications in various areas
including ata mining and databases, eg, in parallel query optimization. In recent years, there …