4 citations · 4 across the 2 of their papers we have counts for
4 papers
Transductive Kernels for Gaussian Processes on Graphs
Yin-Cong Zhi, Felix L. Opolka, Yin Cheng Ng +2
Kernels on graphs have had limited options for node-level problems. To address this, we present a novel, generalized kernel for graphs with node feature data for semi-supervised le…
Gaussian Processes on Graphs via Spectral Kernel Learning
Yin-Cong Zhi, Yin Cheng Ng, Xiaowen Dong
We propose a graph spectrum-based Gaussian process for prediction of signals defined on nodes of the graph. The model is designed to capture various graph signal structures through…
Bayesian Semi-supervised Learning with Graph Gaussian Processes
Yin Cheng Ng, Nicolo Colombo, Ricardo Silva
We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs. The proposed model shows extremely competitive performance w…
A Dynamic Edge Exchangeable Model for Sparse Temporal Networks
Yin Cheng Ng, Ricardo Silva
We propose a dynamic edge exchangeable network model that can capture sparse connections observed in real temporal networks, in contrast to existing models which are dense. The mod…