1

SHAP@ k: Efficient and Probably Approximately Correct (PAC) Identification of Top-k Featuress
Sanjay Kariyappa, Leonidas Tsepenekas, Freddy Lecue, Daniele Magazzeni
PDF
Effcient and Equitable Deployment of Mobile Vaccine Distribution Centers
Da Qi Chen, George Li, Ann Li, Madhav Marathe, Aravind Srinivasan, Leonidas Tsepenekas, Anil Kumar Vullikanti (Alphabetical order)
PDF
Comparing Apples to Oranges: Learning Similarity Functions for Data Produced by Different Distributions
Leonidas Tsepenekas, Ivan Brugere, Freddy Lecue, Daniele Magazzeni
PDF
A New Notion of Individually Fair Clustering: α-Equitable k-Center
Darshan Chakrabarti, John P. Dickerson, Seyed A. Esmaeili, Aravind Srinivasan, Leonidas Tsepenekas (Alphabetical order)
PDF
Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks
Amy Babay, Michael Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti (Alphabetical order)
PDF
Fair Disaster Containment via Graph-Cut Problems
Michael Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti (Alphabetical order)
PDF
Deploying Vaccine Distribution Sites for Improved Accessibility and Equity to Support Pandemic Response
George Li, Ann Li, Madhav Marathe, Aravind Srinivasan, Leonidas Tsepenekas, Anil Kumar Vullikanti (Alphabetical order)
PDF
Approximating Two-Stage Stochastic Supplier Problems
Brian Brubach, Nathaniel Grammel, David G. Harris, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti (Alphabetical order)
PDF
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 (Alphabetical order)
PDF
Probabilistic Fair Clustering
Seyed A. Esmaeili, Brian Brubach, Leonidas Tsepenekas, John P. Dickerson
PDF