4 citations · 4 across the 4 of their papers we have counts for
3 papers · 1 filter
A Lottery Model for Center-type Problems With Outliers
David G. Harris, Thomas Pensyl, Aravind Srinivasan +1
In this paper, we give tight approximation algorithms for the -center and matroid center problems with outliers. Unfairness arises naturally in this setting: certain clients cou…
Dependent randomized rounding for clustering and partition systems with knapsack constraints
David G. Harris, Thomas Pensyl, Aravind Srinivasan +1
Clustering problems are fundamental to unsupervised learning. There is an increased emphasis on fairness in machine learning and AI; one representative notion of fairness is that n…
Fairness in Resource Allocation and Slowed-down Dependent Rounding
David G. Harris, Thomas Pensyl, Aravind Srinivasan +1
We consider an issue of much current concern: could fairness, an issue that is already difficult to guarantee, worsen when algorithms run much of our lives? We consider this in the…