We study the question of fair clustering under the {\em disparate impact} doctrine, where
each protected class must have approximately equal representation in every cluster. We …

We study the problem of finding low-cost {\em fair clusterings} in data where each data point
may belong to many protected groups. Our work significantly generalizes the seminal work …

We analyze the performance of spectral clustering for community extraction in stochastic
block models. We show that, under mild conditions, spectral clustering applied to the …

Clustering is a classic topic in optimization with k-means being one of the most fundamental
such problems. In the absence of any restrictions on the input, the best-known algorithm for k …

In data summarization we want to choose $ k $ prototypes in order to summarize a data set.
We study a setting where the data set comprises several demographic groups and we are …

Dependent rounding is a useful technique for optimization problems with hard budget
constraints. This framework naturally leads to negative correlation properties. However, what …

The (k, z)-clustering problem consists of finding a set of k points called centers, such that the
sum of distances raised to the power of z of every data point to its closest center is …

What does it mean for a clustering to be fair? One popular approach seeks to ensure that
each cluster contains groups in (roughly) the same proportion in which they exist in the …

The stochastic block model (SBM) and its variants have been a popular tool for analyzing
large network data with community structures. In this article, we develop an efficient network …

Finding an informative subset of a large collection of data points or models is at the center of
many problems in computer vision, recommender systems, bio/health informatics as well as …