104 citations · 152 across the 6 of their papers we have counts for
5 papers · 1 filter
Cycle-Balanced Representation Learning For Counterfactual Inference
Guanglin Zhou, Lina Yao, Xiwei Xu +2
With the widespread accumulation of observational data, researchers obtain a new direction to learn counterfactual effects in many domains (e.g., health care and computational adve…
Blockchain-based Trustworthy Federated Learning Architecture
Sin Kit Lo, Yue Liu, Qinghua Lu +4
Federated learning is an emerging privacy-preserving AI technique where clients (i.e., organisations or devices) train models locally and formulate a global model based on the loca…
Architectural Patterns for the Design of Federated Learning Systems
Sin Kit Lo, Qinghua Lu, Liming Zhu +3
Federated learning has received fast-growing interests from academia and industry to tackle the challenges of data hungriness and privacy in machine learning. A federated learning…
Adversarial Examples on Graph Data: Deep Insights into Attack and Defense
Huijun Wu, Chen Wang, Yuriy Tyshetskiy +3
Graph deep learning models, such as graph convolutional networks (GCN) achieve remarkable performance for tasks on graph data. Similar to other types of deep models, graph deep lea…
Interpreting Shared Deep Learning Models via Explicable Boundary Trees
Huijun Wu, Chen Wang, Jie Yin +2
Despite outperforming the human in many tasks, deep neural network models are also criticized for the lack of transparency and interpretability in decision making. The opaqueness r…