Search Results for author: Anil Vullikanti

Found 18 papers, 3 papers with code

Sample Complexity of Opinion Formation on Networks

no code implementations4 Nov 2023 Haolin Liu, Rajmohan Rajaraman, Ravi Sundaram, Anil Vullikanti, Omer Wasim, Haifeng Xu

In this paper, we initialize the study of sample complexity in opinion formation to solve this problem.

Federated Learning

Spatial-Temporal Networks for Antibiogram Pattern Prediction

no code implementations2 May 2023 Xingbo Fu, Chen Chen, Yushun Dong, Anil Vullikanti, Eili Klein, Gregory Madden, Jundong Li

In this paper, we propose a novel problem of antibiogram pattern prediction that aims to predict which patterns will appear in the future.

Simulation-Assisted Optimization for Large-Scale Evacuation Planning with Congestion-Dependent Delays

no code implementations4 Sep 2022 Kazi Ashik Islam, Da Qi Chen, Madhav Marathe, Henning Mortveit, Samarth Swarup, Anil Vullikanti

However, joint optimization of its two essential components, routing and scheduling, with objectives such as minimizing average evacuation time or evacuation completion time, is a computationally hard problem.

Management Scheduling

Differentially Private Partial Set Cover with Applications to Facility Location

no code implementations21 Jul 2022 George Z. Li, Dung Nguyen, Anil Vullikanti

Using our algorithm for Partial Set Cover as a subroutine, we give a differentially private (bicriteria) approximation algorithm for a facility location problem which generalizes $k$-center/$k$-supplier with outliers.

Combinatorial Optimization

Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks

no code implementations16 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.


Deploying Vaccine Distribution Sites for Improved Accessibility and Equity to Support Pandemic Response

1 code implementation9 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.

Differentially Private Community Detection for Stochastic Block Models

no code implementations31 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.

Community Detection Stochastic Block Model

Fair Disaster Containment via Graph-Cut Problems

no code implementations9 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.

Combinatorial Optimization Fairness

Differentially Private Densest Subgraph Detection

no code implementations27 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.

Graph Mining

Parallel Algorithms for Densest Subgraph Discovery Using Shared Memory Model

no code implementations27 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.

Computer Security Spam detection

Mapping Network States Using Connectivity Queries

1 code implementation7 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.

PAC-Learning for Strategic Classification

no code implementations6 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.

Classification General Classification +1

Approximating Two-Stage Stochastic Supplier Problems

no code implementations7 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

Efficient Algorithms for Generating Provably Near-Optimal Cluster Descriptors for Explainability

1 code implementation6 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.

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