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Poster
Probabilistic Fair Clustering
Seyed Esmaeili · Brian Brubach · Leonidas Tsepenekas · John Dickerson

Mon Dec 07 09:00 PM -- 11:00 PM (PST) @ Poster Session 0 #5

In clustering problems, a central decision-maker is given a complete metric graph over vertices and must provide a clustering of vertices that minimizes some objective function. In fair clustering problems, vertices are endowed with a color (e.g., membership in a group), and the requirements of a valid clustering might also include the representation of colors in the solution. Prior work in fair clustering assumes complete knowledge of group membership. In this paper, we generalize this by assuming imperfect knowledge of group membership through probabilistic assignments, and present algorithms in this more general setting with approximation ratio guarantees. We also address the problem of "metric membership", where group membership has a notion of order and distance. Experiments are conducted using our proposed algorithms as well as baselines to validate our approach, and also surface nuanced concerns when group membership is not known deterministically.

Author Information

Seyed Esmaeili (University of Maryland, College Park)
Brian Brubach (University of Maryland)
Leonidas Tsepenekas (University of Maryland)
John Dickerson (University of Maryland)

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