10 citations · 20 across the 10 of their papers we have counts for
12 papers
Morpheus-2.0: Multiscale Probabilistic Forecasting with Dual-Axis Attention
Aakriti, Manoj Cherukumalli, Prathamesh Patil +5
A universal forecaster is trained once and applied everywhere, so it must serve tasks whose schemas, forecast horizons, and temporal resolutions all differ. The number of targets a…
SHAP@k:Efficient and Probably Approximately Correct (PAC) Identification of Top-k Features
Sanjay Kariyappa, Leonidas Tsepenekas, Freddy Lécué +1
The SHAP framework provides a principled method to explain the predictions of a model by computing feature importance. Motivated by applications in finance, we introduce the Top-k…
Comparing Apples to Oranges: Learning Similarity Functions for Data Produced by Different Distributions
Leonidas Tsepenekas, Ivan Brugere, Freddy Lecue +1
Similarity functions measure how comparable pairs of elements are, and play a key role in a wide variety of applications, e.g., notions of Individual Fairness abiding by the semina…
Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks
Amy Babay, Michael Dinitz, Aravind Srinivasan +2
The spread of an epidemic is often modeled by an SIR random process on a social network graph. The MinINF problem for optimal social distancing involves minimizing the expected num…
Deploying Vaccine Distribution Sites for Improved Accessibility and Equity to Support Pandemic Response
George Li, Ann Li, Madhav Marathe +3
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. These social interventions…
Fair Disaster Containment via Graph-Cut Problems
Michael Dinitz, Aravind Srinivasan, Leonidas Tsepenekas +1
Graph cut problems are fundamental in Combinatorial Optimization, and are a central object of study in both theory and practice. Furthermore, the study of \emph{fairness} in Algori…
A New Notion of Individually Fair Clustering: -Equitable -Center
Darshan Chakrabarti, John P. Dickerson, Seyed A. Esmaeili +2
Clustering is a fundamental problem in unsupervised machine learning, and fair variants of it have recently received significant attention due to its societal implications. In this…
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints
Brian Brubach, Darshan Chakrabarti, John P. Dickerson +2
Metric clustering is fundamental in areas ranging from Combinatorial Optimization and Data Mining, to Machine Learning and Operations Research. However, in a variety of situations…
Stochastic Optimization and Learning for Two-Stage Supplier Problems
Brian Brubach, Nathaniel Grammel, David G. Harris +3
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 for…
A Pairwise Fair and Community-preserving Approach to k-Center Clustering
Brian Brubach, Darshan Chakrabarti, John P. Dickerson +3
Clustering is a foundational problem in machine learning with numerous applications. As machine learning increases in ubiquity as a backend for automated systems, concerns about fa…
Probabilistic Fair Clustering
Seyed A. Esmaeili, Brian Brubach, Leonidas Tsepenekas +1
In clustering problems, a central decision-maker is given a complete metric graph over vertices and must provide a clustering of vertices that minimizes some objective function. In…
Message Routing in Wireless and Mobile Networks using TDMA Technology
Timotheos Aslanidis, Leonidas Tsepenekas
In an era where communication has a most important role in modern societies, designing efficient algorithms for data transmission is of the outmost importance. TDMA is a technology…