34 citations · 68 across the 17 of their papers we have counts for
4 papers · 1 filter
Fair Clustering Under a Bounded Cost
Seyed A. Esmaeili, Brian Brubach, Aravind Srinivasan +1
Clustering is a fundamental unsupervised learning problem where a dataset is partitioned into clusters that consist of nearby points in a metric space. A recent variant, fair clust…
Improved Guarantees for Offline Stochastic Matching via New Ordered Contention Resolution Schemes
Brian Brubach, Nathaniel Grammel, Will Ma +2
Matching is one of the most fundamental and broadly applicable problems across many domains. In these diverse real-world applications, there is often a degree of uncertainty in the…
It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks
Michelle Bao, Angela Zhou, Samantha Zottola +5
Risk assessment instrument (RAI) datasets, particularly ProPublica's COMPAS dataset, are commonly used in algorithmic fairness papers due to benchmarking practices of comparing alg…
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints
Brian Brubach, Darshan Chakrabarti, John P. Dickerson +2
Metric clustering is fundamental in areas ranging from Combinatorial Optimization and Data Mining, to Machine Learning and Operations Research. However, in a variety of situations…