13 citations · 42 across the 18 of their papers we have counts for
24 papers
On the largest common subtree of uniform attachment trees
Johannes Bäumler, Céline Kerriou, Bas Lodewijks +4
We study the largest common subtree of two independent unlabeled uniform attachment trees (also known as random recursive trees). Our main result shows that, when the two trees hav…
Image Augmentation as Test Generation for Deep Learning-Based Image Retrieval Systems
Yehan De Silva, Anirudh Sridhar, Armin Lotfy +6
Ensuring the reliability of deep learning-based image retrieval systems is a software engineering challenge. This paper presents a dual contribution: (1) a literature review of aug…
Correlated uniform attachment trees
Johannes Bäumler, Miklós Z. Rácz, Nathan Ross +1
We introduce and study a new model of correlated uniform attachment (UA) trees, where correlation is sprinkled throughout the time evolution of the process. In this model, two UA t…
Finding Super-spreaders in SIS Epidemics
Anirudh Sridhar, Arnob Ghosh
In network epidemic models, controlling the spread of a disease often requires targeted interventions such as vaccinating high-risk individuals based on network structure. However,…
Detecting Mutual Excitations in Non-Stationary Hawkes Processes
Elchanan Mossel, Anirudh Sridhar
We consider the problem of learning the network of mutual excitations (i.e., the dependency graph) in a non-stationary, multivariate Hawkes process. We consider a general setting w…
Detecting Abrupt Changes in Point Processes: Fundamental Limits and Applications
Anna Brandenberger, Elchanan Mossel, Anirudh Sridhar
We consider the problem of detecting abrupt changes (i.e., large jump discontinuities) in the rate function of a point process. The rate function is assumed to be fully unknown, no…
Finding Super-spreaders in Network Cascades
Elchanan Mossel, Anirudh Sridhar
Suppose that a cascade (e.g., an epidemic) spreads on an unknown graph, and only the infection times of vertices are observed. What can be learned about the graph from the infectio…
Quickest Inference of Susceptible-Infected Cascades in Sparse Networks
Anirudh Sridhar, Tirza Routtenberg, H. Vincent Poor
We consider the task of estimating a network cascade as fast as possible. The cascade is assumed to spread according to a general Susceptible-Infected process with heterogeneous tr…
Matching Correlated Inhomogeneous Random Graphs using the -core Estimator
Miklós Z. Rácz, Anirudh Sridhar
We consider the task of estimating the latent vertex correspondence between two edge-correlated random graphs with generic, inhomogeneous structure. We study the so-called \emph{$k…
Average-case and smoothed analysis of graph isomorphism
Julia Gaudio, Miklós Z. Rácz, Anirudh Sridhar
We propose a simple and efficient local algorithm for graph isomorphism which succeeds for a large class of sparse graphs. This algorithm produces a low-depth canonical labeling, w…
Spreading Processes with Mutations over Multi-layer Networks
Mansi Sood, Anirudh Sridhar, Rashad Eletreby +4
A key scientific challenge during the outbreak of novel infectious diseases is to predict how the course of the epidemic changes under different countermeasures that limit interact…
Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach
Sérgio Machado, Anirudh Sridhar, Paulo Gil +3
We study the problem of graph structure identification, i.e., of recovering the graph of dependencies among time series. We model these time series data as components of the state…
Exact Community Recovery in Correlated Stochastic Block Models
Julia Gaudio, Miklos Z. Racz, Anirudh Sridhar
We consider the problem of learning latent community structure from multiple correlated networks. We study edge-correlated stochastic block models with two balanced communities, fo…
Quickest Inference of Network Cascades with Noisy Information
Anirudh Sridhar, H. Vincent Poor
We study the problem of estimating the source of a network cascade given a time series of noisy information about the spread. Initially, there is a single vertex affected by the ca…
The Role of Masks in Mitigating Viral Spread on Networks
Yurun Tian, Anirudh Sridhar, Chai Wah Wu +4
Masks have remained an important mitigation strategy in the fight against COVID-19 due to their ability to prevent the transmission of respiratory droplets between individuals. In…
Correlated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communities
Miklos Z. Racz, Anirudh Sridhar
We consider the task of learning latent community structure from multiple correlated networks. First, we study the problem of learning the latent vertex correspondence between two…
On the Accuracy of Deterministic Models for Viral Spread on Networks
Anirudh Sridhar, Soummya Kar
We consider the emergent behavior of viral spread when agents in a large population interact with each other over a contact network. When the number of agents is large and the cont…
Mean-field Approximations for Stochastic Population Processes with Heterogeneous Interactions
Anirudh Sridhar, Soummya Kar
This paper studies a general class of stochastic population processes in which agents interact with one another over a network. Agents update their behaviors in a random and decent…
Analysis of the Impact of Mask-wearing in Viral Spread: Implications for COVID-19
Yurun Tian, Anirudh Sridhar, Osman Yagan +1
Masks are used as part of a comprehensive strategy of measures to limit transmission and save lives during the COVID-19 pandemic. Research about the impact of mask-wearing in the C…
Correlated randomly growing graphs
Miklos Z. Racz, Anirudh Sridhar
We introduce a new model of correlated randomly growing graphs and study the fundamental questions of detecting correlation and estimating aspects of the correlated structure. The…
Bayes-optimal Methods for Finding the Source of a Cascade
Anirudh Sridhar, H. Vincent Poor
We study the problem of estimating the source of a network cascade. The cascade starts from a single vertex at time 0 and spreads over time, but only a noisy version of the propaga…
Sequential Estimation of Network Cascades
Anirudh Sridhar, H. Vincent Poor
We consider the problem of locating the source of a network cascade, given a noisy time-series of network data. Initially, the cascade starts with one unknown, affected vertex and…
On Distributed Stochastic Gradient Algorithms for Global Optimization
Brian Swenson, Anirudh Sridhar, H. Vincent Poor
The paper considers the problem of network-based computation of global minima in smooth nonconvex optimization problems. It is known that distributed gradient-descent-type algorith…
Client-CASH: Protecting Master Passwords against Offline Attacks
Jeremiah Blocki, Anirudh Sridhar
Offline attacks on passwords are increasingly commonplace and dangerous. An offline adversary is limited only by the amount of computational resources he or she is willing to inves…