5 citations · 7 across the 2 of their papers we have counts for
7 papers
Improved Approximation for Fair Correlation Clustering
Sara Ahmadian, Maryam Negahbani
Correlation clustering is a ubiquitous paradigm in unsupervised machine learning where addressing unfairness is a major challenge. Motivated by this, we study Fair Correlation Clus…
Approximation Algorithms for Continuous Clustering and Facility Location Problems
Deeparnab Chakrabarty, Maryam Negahbani, Ankita Sarkar
We consider the approximability of center-based clustering problems where the points to be clustered lie in a metric space, and no candidate centers are specified. We call such pro…
Better Algorithms for Individually Fair -Clustering
Deeparnab Chakrabarty, Maryam Negahbani
We study data clustering problems with -norm objectives (e.g. -Median and -Means) in the context of individual fairness. The dataset consists of points, and we wa…
Revisiting Priority -Center: Fairness and Outliers
Tanvi Bajpai, Deeparnab Chakrabarty, Chandra Chekuri +1
In the Priority -Center problem, the input consists of a metric space , an integer , and for each point a priority radius . The goal is to choose -c…
Robust -Center with Two Types of Radii
Deeparnab Chakrabarty, Maryam Negahbani
In the non-uniform -center problem, the objective is to cover points in a metric space with specified number of balls of different radii. Chakrabarty, Goyal, and Krishnaswamy [I…
Fair Algorithms for Clustering
Suman K. Bera, Deeparnab Chakrabarty, Nicolas J. Flores +1
We study the problem of finding low-cost Fair Clusterings in data where each data point may belong to many protected groups. Our work significantly generalizes the seminal work of…
Generalized Center Problems with Outliers
Deeparnab Chakrabarty, Maryam Negahbani
We study the -center problem with outliers: given a metric space , a general down-closed family of subsets of , and a parameter , we need to…