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stat.ML2022★ 1 cited
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…
stat.ML2020
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…
stat.ML2020★ 1 cited
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…