219 citations · 1.2k across the 35 of their papers we have counts for
8 papers · 1 filter
An Efficient and Robust System for Vertically Federated Random Forest
Houpu Yao, Jiazhou Wang, Peng Dai +2
As there is a growing interest in utilizing data across multiple resources to build better machine learning models, many vertically federated learning algorithms have been proposed…
Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning
Lianghao Xia, Chao Huang, Yong Xu +4
Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, cri…
Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network
Xiyue Zhang, Chao Huang, Yong Xu +5
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public ri…
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization
Qingsong Zhang, Bin Gu, Cheng Deng +4
Vertical federated learning (VFL) is an effective paradigm of training the emerging cross-organizational (e.g., different corporations, companies and organizations) collaborative l…
Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform
Bo Liu, Chaowei Tan, Jiazhou Wang +7
In this paper, we present Fedlearn-Algo, an open-source privacy preserving machine learning platform. We use this platform to demonstrate our research and development results on pr…
EnsemFDet: An Ensemble Approach to Fraud Detection based on Bipartite Graph
Yuxiang Ren, Hao Zhu, Jiawei Zhang +2
Fraud detection is extremely critical for e-commerce business. It is the intent of the companies to detect and prevent fraud as early as possible. Existing fraud detection methods…
Heterogeneous Deep Graph Infomax
Yuxiang Ren, Bo Liu, Chao Huang +3
Graph representation learning is to learn universal node representations that preserve both node attributes and structural information. The derived node representations can be used…
Greedy Block Coordinate Descent for Large Scale Gaussian Process Regression
Liefeng Bo, Cristian Sminchisescu
We propose a variable decomposition algorithm -greedy block coordinate descent (GBCD)- in order to make dense Gaussian process regression practical for large scale problems. GBCD b…