37 citations · 132 across the 20 of their papers we have counts for
20 papers
Federated k-Core Decomposition: A Secure Distributed Approach
Bin Guo, Emil Sekerinski, Lingyang Chu
As one of the most well-studied cohesive subgraph models, the -core is widely used to find graph nodes that are ``central'' or ``important'' in many applications, such as biolog…
Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices
Qiying Pan, Yifei Zhu, Lingyang Chu
Graph neural networks (GNN) have been widely deployed in real-world networked applications and systems due to their capability to handle graph-structured data. However, the growing…
Revealing Unfair Models by Mining Interpretable Evidence
Mohit Bajaj, Lingyang Chu, Vittorio Romaniello +5
The popularity of machine learning has increased the risk of unfair models getting deployed in high-stake applications, such as justice system, drug/vaccination design, and medical…
Cosine Model Watermarking Against Ensemble Distillation
Laurent Charette, Lingyang Chu, Yizhou Chen +3
Many model watermarking methods have been developed to prevent valuable deployed commercial models from being stealthily stolen by model distillations. However, watermarks produced…
Mining Minority-class Examples With Uncertainty Estimates
Gursimran Singh, Lingyang Chu, Lanjun Wang +3
In the real world, the frequency of occurrence of objects is naturally skewed forming long-tail class distributions, which results in poor performance on the statistically rare cla…
Achieving Model Fairness in Vertical Federated Learning
Changxin Liu, Zhenan Fan, Zirui Zhou +4
Vertical federated learning (VFL) has attracted greater and greater interest since it enables multiple parties possessing non-overlapping features to strengthen their machine learn…
Training Fair Models in Federated Learning without Data Privacy Infringement
Xin Che, Jingdi Hu, Zirui Zhou +2
Training fair machine learning models becomes more and more important. As many powerful models are trained by collaboration among multiple parties, each holding some sensitive data…
Finding Representative Interpretations on Convolutional Neural Networks
Peter Cho-Ho Lam, Lingyang Chu, Maxim Torgonskiy +3
Interpreting the decision logic behind effective deep convolutional neural networks (CNN) on images complements the success of deep learning models. However, the existing methods c…
Robust Counterfactual Explanations on Graph Neural Networks
Mohit Bajaj, Lingyang Chu, Zi Yu Xue +4
Massive deployment of Graph Neural Networks (GNNs) in high-stake applications generates a strong demand for explanations that are robust to noise and align well with human intuitio…
Model Complexity of Deep Learning: A Survey
Xia Hu, Lingyang Chu, Jian Pei +2
Model complexity is a fundamental problem in deep learning. In this paper we conduct a systematic overview of the latest studies on model complexity in deep learning. Model complex…
Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test
Zicun Cong, Lingyang Chu, Yu Yang +1
The Kolmogorov-Smirnov (KS) test is popularly used in many applications, such as anomaly detection, astronomy, database security and AI systems. One challenge remained untouched is…
Personalized Cross-Silo Federated Learning on Non-IID Data
Yutao Huang, Lingyang Chu, Zirui Zhou +4
Non-IID data present a tough challenge for federated learning. In this paper, we explore a novel idea of facilitating pairwise collaborations between clients with similar data. We…
Finding Route Hotspots in Large Labeled Networks
Mingtao Lei, Xi Zhang, Lingyang Chu +3
In many advanced network analysis applications, like social networks, e-commerce, and network security, hotspots are generally considered as a group of vertices that are tightly co…
Exact and Consistent Interpretation of Piecewise Linear Models Hidden behind APIs: A Closed Form Solution
Zicun Cong, Lingyang Chu, Lanjun Wang +2
More and more AI services are provided through APIs on cloud where predictive models are hidden behind APIs. To build trust with users and reduce potential application risk, it is…
Mining Top-k Sequential Patterns in Database Graphs:A New Challenging Problem and a Sampling-based Approach
Mingtao Lei, Lingyang Chu, Zhefeng Wang
In many real world networks, a vertex is usually associated with a transaction database that comprehensively describes the behaviour of the vertex. A typical example is the social…
Mining Density Contrast Subgraphs
Yu Yang, Lingyang Chu, Yanyan Zhang +3
Dense subgraph discovery is a key primitive in many graph mining applications, such as detecting communities in social networks and mining gene correlation from biological data. Mo…
Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution
Lingyang Chu, Xia Hu, Juhua Hu +2
Strong intelligent machines powered by deep neural networks are increasingly deployed as black boxes to make decisions in risk-sensitive domains, such as finance and medical. To re…
Finding Theme Communities from Database Networks
Lingyang Chu, Zhefeng Wang, Jian Pei +3
Given a database network where each vertex is associated with a transaction database, we are interested in finding theme communities. Here, a theme community is a cohesive subgraph…
Activity Maximization by Effective Information Diffusion in Social Networks
Zhefeng Wang, Yu Yang, Jian Pei +1
In a social network, even about the same information the excitements between different pairs of users are different. If you want to spread a piece of new information and maximize t…
ALID: Scalable Dominant Cluster Detection
Lingyang Chu, Shuhui Wang, Siyuan Liu +2
Detecting dominant clusters is important in many analytic applications. The state-of-the-art methods find dense subgraphs on the affinity graph as the dominant clusters. However, t…