4 citations · 6 across the 4 of their papers we have counts for
4 papers
Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains
Haitao Liu, Kai Wu, Yew-Soon Ong +3
Multi-task Gaussian process (MTGP) is a well-known non-parametric Bayesian model for learning correlated tasks effectively by transferring knowledge across tasks. But current MTGPs…
Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems
Haitao Liu, Changjun Liu, Xiaomo Jiang +3
The demand of probabilistic time series forecasting has been recently raised in various dynamic system scenarios, for example, system identification and prognostic and health manag…
Modulating Scalable Gaussian Processes for Expressive Statistical Learning
Haitao Liu, Yew-Soon Ong, Xiaomo Jiang +1
For a learning task, Gaussian process (GP) is interested in learning the statistical relationship between inputs and outputs, since it offers not only the prediction mean but also…
Deep Latent-Variable Kernel Learning
Haitao Liu, Yew-Soon Ong, Xiaomo Jiang +1
Deep kernel learning (DKL) leverages the connection between Gaussian process (GP) and neural networks (NN) to build an end-to-end, hybrid model. It combines the capability of NN to…