no code implementations • 16 Feb 2022 • Amy Babay, Michael Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti
The second is a Sample Average Approximation (SAA) based algorithm, which we analyze for the Chung-Lu random graph model.
1 code implementation • 9 Feb 2022 • George Li, Ann Li, Madhav Marathe, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti
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.
no code implementations • 31 Jan 2022 • Mohamed Seif, Dung Nguyen, Anil Vullikanti, Ravi Tandon
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.
no code implementations • 9 Jun 2021 • Michael Dinitz, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti
Graph cut problems are fundamental in Combinatorial Optimization, and are a central object of study in both theory and practice.
no code implementations • 27 May 2021 • Dung Nguyen, Anil Vullikanti
We study the densest subgraph problem in the edge privacy model, in which the edges of the graph are private.
no code implementations • 27 Feb 2021 • B. D. M. De Zoysa, Y. A. M. M. A. Ali, M. D. I. Maduranga, Indika Perera, Saliya Ekanayake, Anil Vullikanti
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.
1 code implementation • 7 Dec 2020 • Alexander Rodríguez, Bijaya Adhikari, Andrés D. González, Charles Nicholson, Anil Vullikanti, B. Aditya Prakash
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.
no code implementations • 6 Dec 2020 • Ravi Sundaram, Anil Vullikanti, Haifeng Xu, Fan Yao
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.
no code implementations • 21 Sep 2020 • Aniruddha Adiga, Devdatt Dubhashi, Bryan Lewis, Madhav Marathe, Srinivasan Venkatramanan, Anil Vullikanti
COVID-19 pandemic represents an unprecedented global health crisis in the last 100 years.
no code implementations • 7 Aug 2020 • Brian Brubach, Nathaniel Grammel, David G. Harris, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti
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
1 code implementation • 6 Feb 2020 • Prathyush Sambaturu, Aparna Gupta, Ian Davidson, S. S. Ravi, Anil Vullikanti, Andrew Warren
Improving the explainability of the results from machine learning methods has become an important research goal.