In this paper, we study the individual preference (IP) stability, which is an notion capturing
individual fairness and stability in clustering. Within this setting, a clustering is $\alpha $-IP …

We present a scalable algorithm for the individually fair ($ p $, $ k $)-clustering problem
introduced by Jung et al. and Mahabadi et al. Given $ n $ points $ P $ in a metric space, let …

The importance of dealing with big data is further increasing, as machine learning (ML)
systems obtain useful knowledge from big datasets. However, using all data is practically …

There has been much recent interest in developing fair clustering algorithms that seek to do
justice to the representation of groups defined along sensitive attributes such as race and …

Hashmap is a fundamental data structure in computer science. There has been extensive
research on constructing hashmaps that minimize the number of collisions leading to …

The representative k-median problem generalizes the classical clustering formulations in
that it partitions the data points into several disjoint demographic groups and poses a lower …

Clustering is a fundamental problem in machine learning and operations research.
Therefore, given the fact that fairness considerations have become of paramount importance …

The k-center clustering problem is of fundamental importance for a broad range of machine
learning and data science applications. In this paper, we study the deletion-robust version of …

Low-rank approximation and column subset selection are two fundamental and related
problems that are applied across a wealth of machine learning applications. In this paper …

There is a large amount of work constructing hashmaps to minimize the number of collisions.
However, to the best of our knowledge no known hashing technique guarantees group …