125 citations · 311 across the 20 of their papers we have counts for
22 papers
Separable mixing: the general formulation and a particular example focusing on mask efficiency
M. C. J. Bootsma, K. M. D. Chan, O. Diekmann +1
The aim of this short note is twofold. We formulate the general Kermack-McKendrick epidemic model incorporating static heterogeneity and show how it simplifies to a scalar Renewal…
A versatile stochastic dissemination model
K. M. D. Chan, M. R. H. Mandjes
This paper consider a highly general dissemination model that keeps track of the stochastic evolution of the distribution of wealth over a set of agents. There are two types of eve…
Adversarial Plannning
Valentin Vie, Ryan Sheatsley, Sophia Beyda +4
Planning algorithms are used in computational systems to direct autonomous behavior. In a canonical application, for example, planning for autonomous vehicles is used to automate t…
Joint Coreset Construction and Quantization for Distributed Machine Learning
Hanlin Lu, Changchang Liu, Shiqiang Wang +4
Coresets are small, weighted summaries of larger datasets, aiming at providing provable error bounds for machine learning (ML) tasks while significantly reducing the communication…
Communication-efficient k-Means for Edge-based Machine Learning
Hanlin Lu, Ting He, Shiqiang Wang +5
We consider the problem of computing the k-means centers for a large high-dimensional dataset in the context of edge-based machine learning, where data sources offload machine lear…
Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions
Todd Huster, Jeremy E. J. Cohen, Zinan Lin +5
Generative adversarial networks (GANs) are often billed as "universal distribution learners", but precisely what distributions they can represent and learn is still an open questio…
You Do (Not) Belong Here: Detecting DPI Evasion Attacks with Context Learning
Shitong Zhu, Shasha Li, Zhongjie Wang +5
As Deep Packet Inspection (DPI) middleboxes become increasingly popular, a spectrum of adversarial attacks have emerged with the goal of evading such middleboxes. Many of these att…
Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistency
Shasha Li, Shitong Zhu, Sudipta Paul +5
There has been a recent surge in research on adversarial perturbations that defeat Deep Neural Networks (DNNs) in machine vision; most of these perturbation-based attacks target ob…
Sharing Models or Coresets: A Study based on Membership Inference Attack
Hanlin Lu, Changchang Liu, Ting He +2
Distributed machine learning generally aims at training a global model based on distributed data without collecting all the data to a centralized location, where two different appr…
Rapid Top-Down Synthesis of Large-Scale IoT Networks
Pradipta Ghosh, Jonathan Bunton, Dimitrios Pylorof +6
Advances in optimization and constraint satisfaction techniques, together with the availability of elastic computing resources, have spurred interest in large-scale network verific…
Topological Effects on Attacks Against Vertex Classification
Benjamin A. Miller, Mustafa Çamurcu, Alexander J. Gomez +2
Vertex classification is vulnerable to perturbations of both graph topology and vertex attributes, as shown in recent research. As in other machine learning domains, concerns about…
Node Immunization with Non-backtracking Eigenvalues
Leo Torres, Kevin S. Chan, Hanghang Tong +1
The non-backtracking matrix and its eigenvalues have many applications in network science and graph mining, such as node and edge centrality, community detection, length spectrum t…
A4 : Evading Learning-based Adblockers
Shitong Zhu, Zhongjie Wang, Xun Chen +6
Efforts by online ad publishers to circumvent traditional ad blockers towards regaining fiduciary benefits, have been demonstrably successful. As a result, there have recently emer…
GLEE: Geometric Laplacian Eigenmap Embedding
Leo Torres, Kevin S Chan, Tina Eliassi-Rad
Graph embedding seeks to build a low-dimensional representation of a graph G. This low-dimensional representation is then used for various downstream tasks. One popular approach is…
Learning and Planning in the Feature Deception Problem
Zheyuan Ryan Shi, Ariel D. Procaccia, Kevin S. Chan +5
Today's high-stakes adversarial interactions feature attackers who constantly breach the ever-improving security measures. Deception mitigates the defender's loss by misleading the…
Robust Coreset Construction for Distributed Machine Learning
Hanlin Lu, Ming-Ju Li, Ting He +3
Coreset, which is a summary of the original dataset in the form of a small weighted set in the same sample space, provides a promising approach to enable machine learning over dist…
Polarization and Fluctuations in Signed Social Networks
Pedro Cisneros-Velarde, Kevin S. Chan, Francesco Bullo
Much recent research on social networks has focused on the modeling and analysis of how opinions evolve as a function of interpersonal relationships. It is also of great interest t…
Modeling the Resource Requirements of Convolutional Neural Networks on Mobile Devices
Zongqing Lu, Swati Rallapalli, Kevin Chan +1
Convolutional Neural Networks (CNNs) have revolutionized the research in computer vision, due to their ability to capture complex patterns, resulting in high inference accuracies.…
Influence of Personal Preferences on Link Dynamics in Social Networks
Ashwin Bahulkar, Boleslaw K. Szymanski, Nitesh Chawla +2
We study a unique network dataset including periodic surveys and electronic logs of dyadic contacts via smartphones. The participants were a sample of freshmen entering university…
Information Cascades in Feed-based Networks of Users with Limited Attention
Sameet Sreenivasan, Kevin S. Chan, Ananthram Swami +2
We build a model of information cascades on feed-based networks, taking into account the finite attention span of users, message generation rates and message forwarding rates. Usin…
Dynamic Service Migration in Mobile Edge Computing Based on Markov Decision Process
Shiqiang Wang, Rahul Urgaonkar, Murtaza Zafer +3
In mobile edge computing, local edge servers can host cloud-based services, which reduces network overhead and latency but requires service migrations as users move to new location…
Mobility-Induced Service Migration in Mobile Micro-Clouds
Shiqiang Wang, Rahul Urgaonkar, Ting He +3
Mobile micro-cloud is an emerging technology in distributed computing, which is aimed at providing seamless computing/data access to the edge of the network when a centralized serv…