Search Results for author:
Found 11 papers, 3 papers with code
Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks
The second is a Sample Average Approximation (SAA) based algorithm, which we analyze for the Chung-Lu random graph model.
Deploying Vaccine Distribution Sites for Improved Accessibility and Equity to Support Pandemic Response
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.
Differentially Private Community Detection for Stochastic Block Models
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.
Fair Disaster Containment via Graph-Cut Problems
Graph cut problems are fundamental in Combinatorial Optimization, and are a central object of study in both theory and practice.
Differentially Private Densest Subgraph Detection
We study the densest subgraph problem in the edge privacy model, in which the edges of the graph are private.
Parallel Algorithms for Densest Subgraph Discovery Using Shared Memory Model
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.
Mapping Network States Using Connectivity Queries
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.
PAC-Learning for Strategic Classification
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.
Models for COVID-19 Pandemic: A Comparative Analysis
COVID-19 pandemic represents an unprecedented global health crisis in the last 100 years.
Approximating Two-Stage Stochastic Supplier Problems
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
Efficient Algorithms for Generating Provably Near-Optimal Cluster Descriptors for Explainability
Improving the explainability of the results from machine learning methods has become an important research goal.
