Search Results for author: Leonidas Tsepenekas

Found 6 papers, 2 papers with code

A New Notion of Individually Fair Clustering: $α$-Equitable $k$-Center

1 code implementation9 Jun 2021 Darshan Chakrabarti, John P. Dickerson, Seyed A. Esmaeili, Aravind Srinivasan, Leonidas Tsepenekas

Clustering is a fundamental problem in unsupervised machine learning, and fair variants of it have recently received significant attention due to its societal implications.

Fairness

Fair Disaster Containment via Graph-Cut Problems

no code implementations9 Jun 2021 Michael Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti

Graph cut problems are fundamental in Combinatorial Optimization, and are a central object of study in both theory and practice.

Combinatorial Optimization Fairness

Approximating Two-Stage Stochastic Supplier Problems

no code implementations7 Aug 2020 Brian Brubach, Nathaniel Grammel, David G. Harris, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti

The main focus of this paper is radius-based (supplier) clustering in the two-stage stochastic setting with recourse, where the inherent stochasticity of the model comes in the form of a budget constraint.

Data Structures and Algorithms

Probabilistic Fair Clustering

no code implementations NeurIPS 2020 Seyed A. Esmaeili, Brian Brubach, Leonidas Tsepenekas, John P. Dickerson

In fair clustering problems, vertices are endowed with a color (e. g., membership in a group), and the features of a valid clustering might also include the representation of colors in that clustering.

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