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.
no code implementations • ICML 2020 • Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Samir Khuller, Aravind Srinivasan, Leonidas Tsepenekas
Clustering is a foundational problem in machine learning with numerous applications.
no code implementations • 7 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
1 code implementation • 2 Mar 2021 • Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Aravind Srinivasan, Leonidas Tsepenekas
Metric clustering is fundamental in areas ranging from Combinatorial Optimization and Data Mining, to Machine Learning and Operations Research.
no code implementations • 9 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.
1 code implementation • 9 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.
1 code implementation • 9 Feb 2022 • George Li, Ann Li, Madhav Marathe, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti
In response to COVID-19, many countries have mandated social distancing and banned large group gatherings in order to slow down the spread of SARS-CoV-2.
no code implementations • 16 Feb 2022 • Amy Babay, Michael Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti
The second is a Sample Average Approximation (SAA) based algorithm, which we analyze for the Chung-Lu random graph model.
no code implementations • 10 Jul 2023 • Sanjay Kariyappa, Leonidas Tsepenekas, Freddy Lécué, Daniele Magazzeni
While any method to compute SHAP values with uncertainty estimates (such as KernelSHAP and SamplingSHAP) can be trivially adapted to solve TkIP, doing so is highly sample inefficient.