The second is a Sample Average Approximation (SAA) based algorithm, which we analyze for the Chung-Lu random graph model.
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.
To the best of our knowledge, this is the first work to study the impact of privacy constraints on the fundamental limits for community detection.
Graph cut problems are fundamental in Combinatorial Optimization, and are a central object of study in both theory and practice.
The problem of finding dense components of a graph is a widely explored area in data analysis, with diverse applications in fields and branches of study including community mining, spam detection, computer security and bioinformatics.
In contrast, we study the harder problem of inferring failed components given partial information of some `serviceable' reachable nodes and a small sample of point probes, being the first often more practical to obtain.
In this paper, we generalize both of these through a unified framework for strategic classification, and introduce the notion of strategic VC-dimension (SVC) to capture the PAC-learnability in our general strategic setup.
COVID-19 pandemic represents an unprecedented global health crisis in the last 100 years.
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 form of a budget constraint.
Data Structures and Algorithms
Improving the explainability of the results from machine learning methods has become an important research goal.