Publications

Leonidas Tsepenekas, Ivan Brugere (2022). Comparing Apples to Oranges: Learning Similarity Functions for Data Produced by Different Distributions. Under Review.

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Michael Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti (Alphabetical order) (2022). Fair Disaster Containment via Graph-Cut Problems. AISTATS 2022.

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Amy Babay, Michael Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti (Alphabetical order) (2022). Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks. AISTATS 2022.

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Darshan Chakrabarti, John P. Dickerson, Seyed A. Esmaeili, Aravind Srinivasan, Leonidas Tsepenekas (Alphabetical order) (2022). A New Notion of Individually Fair Clustering: α-Equitable k-Center. AISTATS 2022.

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George Li, Ann Li, Madhav Marathe, Aravind Srinivasan, Leonidas Tsepenekas, Anil Kumar Vullikanti (Alphabetical order) (2021). Deploying Vaccine Distribution Sites for Improved Accessibility and Equity to Support Pandemic Response. AAMAS 2022 (Best Student Paper Award).

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Brian Brubach, Nathaniel Grammel, David G. Harris, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti (Alphabetical order) (2021). Approximating Two-Stage Stochastic Supplier Problems. APPROX-RANDOM 2021.

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Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Aravind Srinivasan, Leonidas Tsepenekas (Alphabetical order) (2021). Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints. AAAI 2021.

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Seyed A. Esmaeili, Brian Brubach, Leonidas Tsepenekas, John P. Dickerson (2020). Probabilistic Fair Clustering. NeurIPS 2020.

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Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Samir Khuller, Aravind Srinivasan, Leonidas Tsepenekas (Alphabetical order) (2020). A Pairwise Fair and Community-preserving Approach to k-Center Clustering. ICML 2020.

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