367 citations · 1.3k across the 62 of their papers we have counts for
8 papers · 1 filter
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…
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…
Scalable Gaussian Process Classification with Additive Noise for Various Likelihoods
Haitao Liu, Yew-Soon Ong, Ziwei Yu +2
Gaussian process classification (GPC) provides a flexible and powerful statistical framework describing joint distributions over function space. Conventional GPCs however suffer fr…
Understanding and Comparing Scalable Gaussian Process Regression for Big Data
Haitao Liu, Jianfei Cai, Yew-Soon Ong +1
As a non-parametric Bayesian model which produces informative predictive distribution, Gaussian process (GP) has been widely used in various fields, like regression, classification…
Large-scale Heteroscedastic Regression via Gaussian Process
Haitao Liu, Yew-Soon Ong, Jianfei Cai
Heteroscedastic regression considering the varying noises among observations has many applications in the fields like machine learning and statistics. Here we focus on the heterosc…
When Gaussian Process Meets Big Data: A Review of Scalable GPs
Haitao Liu, Yew-Soon Ong, Xiaobo Shen +1
The vast quantity of information brought by big data as well as the evolving computer hardware encourages success stories in the machine learning community. In the meanwhile, it po…
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression
Haitao Liu, Jianfei Cai, Yi Wang +1
In order to scale standard Gaussian process (GP) regression to large-scale datasets, aggregation models employ factorized training process and then combine predictions from distrib…