19 citations · 32 across the 5 of their papers we have counts for
6 papers
Complementary Supervised and Self-Supervised Representations for Out-of-Distribution Graph Learning
Qingying Hao, Zikang Chen, Chuxuan Hu +4
Out-of-distribution (OOD) generalization remains challenging for graph neural networks (GNNs), as graph distributions can vary substantially across time and domains. Supervised and…
EDoG: Adversarial Edge Detection For Graph Neural Networks
Xiaojun Xu, Yue Yu, Hanzhang Wang +3
Graph Neural Networks (GNNs) have been widely applied to different tasks such as bioinformatics, drug design, and social networks. However, recent studies have shown that GNNs are…
A Tagging Solution to Discover IoT Devices in Apartments
Berkay Kaplan, Jingyu Qian, Israel J Lopez-Toledo +1
The number of IoT devices in smart homes is increasing. This broad adoption facilitates users' lives, but it also brings problems. One such issue is that some IoT devices may invad…
Compromised ACC vehicles can degrade current mixed-autonomy traffic performance while remaining stealthy against detection
George Gunter, Huichen Li, Avesta Hojjati +6
We demonstrate that a supply-chain level compromise of the adaptive cruise control (ACC) capability on equipped vehicles can be used to significantly degrade system level performan…
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators
Yunhui Long, Boxin Wang, Zhuolin Yang +4
Recent advances in machine learning have largely benefited from the massive accessible training data. However, large-scale data sharing has raised great privacy concerns. In this w…
Plausible Deniability for Privacy-Preserving Data Synthesis
Vincent Bindschaedler, Reza Shokri, Carl A. Gunter
Releasing full data records is one of the most challenging problems in data privacy. On the one hand, many of the popular techniques such as data de-identification are problematic…