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 Chierichetti et al. (NIPS 2017)
We demonstrate the use of our technique by obtaining substantially improved results for two different on-line problems. CCF-A 447 被引用 · 1 笔记 引用 Fair Algorithms for Clustering Suman K. BeraDeeparnab ChakrabartyNicolas FloresMaryam Negahbani arXiv: Data Structures and Algorithms Cornell ...
They then applied k-means or k-median algorithms to the set of fairlets. A majority of follow-up studies since then have taken a combinatorial approach to search for clustering with a balance closer to the desired level [7], [16]. However, the computational complexity of such approaches ...
The classic Cluster Editing problem (also known as Correlation Clustering) asks to transform a given graph into a disjoint union of cliques (clusters) by a
The classic Cluster Editing problem (also known as Correlation Clustering) asks to transform a given graph into a disjoint union of cliques (clusters) by a small number of edge modifications. When applied to vertex-colored graphs (the colors representing subgroups), standard algorithms for the NP...
For clustering, the property is that the system eventually reaches a configuration with N clusters of the same type, while for the other population protocols, the property is that the system reaches a stable configuration. The experiments show that our encoding of fairness into systems is viable ...
Glaucoma is the leading cause of irreversible blindness globally. Research indicates a disproportionate impact of glaucoma on racial and ethnic minorities. Existing deep learning models for glaucoma detection might not achieve equitable performance across diverse identity groups. We developed fair identify no...
Algorithms for fair k-clustering with multiple protected attributes We study fair center based clustering problems. In an influential paper, Chierichetti, Kumar, Lattanzi and Vassilvitskii (NIPS 2017) consider the problem o... M Bhm,A Fazzone,S Leonardi,... - 《Oper.res.lett》 被引量: 0发...
Compositional fairness constraints for graph embeddings Intl. Conf. on Mach. Learn. (ICML) (2019) Google Scholar [5] X. Zhang, Q. Wang A Unified Framework for Fair Spectral Clustering With Effective Graph Learning arXiv (2023) Google Scholar [6] O.D. Kose, G. Mateos, Y. Shen Fairness...
TB0 (Depricated: time bucket refrence for micromegas) EntTB (position in time buckets for detector entrance) Has options for RANSAC, Hierarchical Clustering, and Houghs algorithms for track finding. record of hits (TClonesArray of AtEvent) -> record of reconstructed tracks (ATTrack) (TClones...