Message Routing in Wireless and Mobile Networks using TDMA Technology. - Timotheos Aslanidis; Leonidas Tsepenekas (CoRR, 2016)

A Unified Approach to Online Matching with Conflict-Aware Constraints. - Pan Xu 001; Yexuan Shi; Hao Cheng; John P. Dickerson; Karthik Abinav Sankararaman; Aravind Srinivasan; Yongxin Tong; Leonidas Tsepenekas (AAAI, 2019)

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

Probabilistic Fair Clustering. - Seyed A. Esmaeili; Brian Brubach; Leonidas Tsepenekas; John Dickerson 001 (NeurIPS, 2020)

Approximation Algorithms for Radius-Based, Two-Stage Stochastic Clustering Problems with Budget Constraints. - Brian Brubach; Nathaniel Grammel; David G. Harris 001; Aravind Srinivasan; Leonidas Tsepenekas; Anil Vullikanti (CoRR, 2020)

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

Approximating Two-Stage Stochastic Supplier Problems. - Brian Brubach; Nathaniel Grammel; David G. Harris 001; Aravind Srinivasan; Leonidas Tsepenekas; Anil Vullikanti (APPROX-RANDOM, 2021)

Fair Disaster Containment via Graph-Cut Problems. - Amy Babay; Michael Dinitz; Prathyush Sambaturu; Aravind Srinivasan; Leonidas Tsepenekas; Anil Vullikanti (CoRR, 2021)

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

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

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

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

On Algorithmic Fairness and Stochastic Models for Combinatorial Optimization and Unsupervised Machine Learning. - Leonidas Tsepenekas (2022)

Efficient and Equitable Deployment of Mobile Vaccine Distribution Centers. - Da Qi Chen; Ann Li; George Z. Li; Madhav V. Marathe; Aravind Srinivasan; Leonidas Tsepenekas; Anil Vullikanti (IJCAI, 2023)

Comparing Apples to Oranges: Learning Similarity Functions for Data Produced by Different Distributions. - Leonidas Tsepenekas; Ivan Brugere; Freddy Lécué; Daniele Magazzeni (NeurIPS, 2023)

Deploying vaccine distribution sites for improved accessibility and equity to support pandemic response. - George Z. Li; Ann Li; Madhav V. Marathe; Aravind Srinivasan; Leonidas Tsepenekas; Anil Vullikanti (Auton. Agents Multi Agent Syst., 2023)

SHAP@k: Efficient and Probably Approximately Correct (PAC) Identification of Top-K Features. - Sanjay Kariyappa; Leonidas Tsepenekas; Freddy Lécué; Daniele Magazzeni (AAAI, 2024)