Better Algorithms for Individually Fair $k$-Clustering

1 code implementation • NeurIPS 2021 • Deeparnab Chakrabarty, Maryam Negahbani

In this paper, our main contribution is to use Linear Programming (LP) techniques to obtain better algorithms for this problem, both in theory and in practice.

Clustering Fairness

Revisiting Priority $k$-Center: Fairness and Outliers

no code implementations • 4 Mar 2021 • Tanvi Bajpai, Deeparnab Chakrabarty, Chandra Chekuri, Maryam Negahbani

In the Priority $k$-Center problem, the input consists of a metric space $(X, d)$, an integer $k$, and for each point $v \in X$ a priority radius $r(v)$.

Clustering Fairness +1

Fair Algorithms for Clustering

1 code implementation • NeurIPS 2019 • Suman K. Bera, Deeparnab Chakrabarty, Nicolas J. Flores, Maryam Negahbani

- We allow the user to specify the parameters that define fair representation.

Clustering Fairness

Provable Submodular Minimization using Wolfe's Algorithm

no code implementations • NeurIPS 2014 • Deeparnab Chakrabarty, Prateek Jain, Pravesh Kothari

In 1976, Wolfe proposed an algorithm to find the minimum Euclidean norm point in a polytope, and in 1980, Fujishige showed how Wolfe's algorithm can be used for SFM.