most citedAdversarial Model Extraction on Graph Neural Networks

9 citations · 27 across the 7 of their papers we have counts for

collaborators

7 papers

cs.SI2020

Analyzing Societal Impact of COVID-19: A Study During the Early Days of the Pandemic

Swaroop Gowdra Shanthakumar, Anand Seetharam, Arti Ramesh

In this paper, we collect and study Twitter communications to understand the societal impact of COVID-19 in the United States during the early days of the pandemic. With infections…

cs.SI2020

Characterizing Human Mobility Patterns During COVID-19 using Cellular Network Data

Necati A. Ayan, Nilson L. Damasceno, Sushil Chaskar +4

In this paper, our goal is to analyze and compare cellular network usage data from pre-lockdown, during lockdown, and post-lockdown phases surrounding the COVID-19 pandemic to unde…

cs.LG20207 cited

RelEx: A Model-Agnostic Relational Model Explainer

Yue Zhang, David Defazio, Arti Ramesh

In recent years, considerable progress has been made on improving the interpretability of machine learning models. This is essential, as complex deep learning models with millions…

cs.SI20208 cited

Understanding the Socio-Economic Disruption in the United States during COVID-19's Early Days

Swaroop Gowdra Shanthakumar, Anand Seetharam, Arti Ramesh

In this paper, we collect and study Twitter communications to understand the socio-economic impact of COVID-19 in the United States during the early days of the pandemic. Our analy…

cs.LG20201 cited

Struct-MMSB: Mixed Membership Stochastic Blockmodels with Interpretable Structured Priors

Yue Zhang, Arti Ramesh

The mixed membership stochastic blockmodel (MMSB) is a popular framework for community detection and network generation. It learns a low-rank mixed membership representation for ea…

cs.LG20202 cited

Learning Fairness-aware Relational Structures

Yue Zhang, Arti Ramesh

The development of fair machine learning models that effectively avert bias and discrimination is an important problem that has garnered attention in recent years. The necessity of…