17 citations · 27 across the 4 of their papers we have counts for
5 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…
Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages
Yin Cheng Ng, Pawel Chilinski, Ricardo Silva
Factorial Hidden Markov Models (FHMMs) are powerful models for sequential data but they do not scale well with long sequences. We propose a scalable inference and learning algorith…