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Leonidas Tsepenekas

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SHAP@k:Efficient and Probably Approximately Correct (PAC) Identification of Top-k Features

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Jul 10, 2023
Sanjay Kariyappa, Leonidas Tsepenekas, Freddy Lécué, Daniele Magazzeni

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Comparing Apples to Oranges: Learning Similarity Functions for Data Produced by Different Distributions

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Aug 26, 2022
Leonidas Tsepenekas, Ivan Brugere

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Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks

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Feb 16, 2022
Amy Babay, Michael Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti

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Deploying Vaccine Distribution Sites for Improved Accessibility and Equity to Support Pandemic Response

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Feb 09, 2022
George Li, Ann Li, Madhav Marathe, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti

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Fair Disaster Containment via Graph-Cut Problems

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Jun 09, 2021
Amy Babay, Michael Dinitz, Prathyush Sambaturu, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti

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

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Jun 09, 2021
Darshan Chakrabarti, John P. Dickerson, Seyed A. Esmaeili, Aravind Srinivasan, Leonidas Tsepenekas

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Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints

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Mar 02, 2021
Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Aravind Srinivasan, Leonidas Tsepenekas

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A Pairwise Fair and Community-preserving Approach to k-Center Clustering

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Jul 14, 2020
Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Samir Khuller, Aravind Srinivasan, Leonidas Tsepenekas

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Probabilistic Fair Clustering

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Jun 19, 2020
Seyed A. Esmaeili, Brian Brubach, Leonidas Tsepenekas, John P. Dickerson

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