When Machine Learning technologies are used in contexts that affect citizens, companies as
well as researchers need to be confident that there will not be any unexpected social …

Clustering algorithms are a class of unsupervised machine learning (ML) algorithms that
feature ubiquitously in modern data science, and play a key role in many learning-based …

As machine learning has become more prevalent, researchers have begun to recognize the
necessity of ensuring machine learning systems are fair. Recently, there has been an …

We present a new approach for solving (minimum disagreement) correlation clustering that
results in sublinear algorithms with highly efficient time and space complexity for this …

We introduce fast algorithms for correlation clustering with respect to the Min Max objective
that provide constant factor approximations on complete graphs. Our algorithms are the first …

Clustering is a fundamental unsupervised learning problem where a dataset is partitioned
into clusters that consist of nearby points in a metric space. A recent variant, fair clustering …

Correlation clustering is a fundamental optimization problem at the intersection of machine
learning and theoretical computer science. Motivated by applications to big data processing …

We show that a simple single-pass semi-streaming variant of the Pivot algorithm for
Correlation Clustering gives a (3+ eps)-approximation using O (n/eps) words of memory …

Correlation clustering is a ubiquitous paradigm in unsupervised machine learning where
addressing unfairness is a major challenge. Motivated by this, we study fair correlation …

We show that a simple single-pass semi-streaming variant of the Pivot algorithm for
Correlation Clustering gives a (3+{\epsilon})-approximation using O (n/{\epsilon}) words of …