Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162582
Title: Modulating scalable Gaussian processes for expressive statistical learning
Authors: Liu, Haitao
Ong, Yew-Soon
Jiang, Xiaomo
Wang, Xiaofang
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Liu, H., Ong, Y., Jiang, X. & Wang, X. (2021). Modulating scalable Gaussian processes for expressive statistical learning. Pattern Recognition, 120, 108121-. https://dx.doi.org/10.1016/j.patcog.2021.108121
Journal: Pattern Recognition
Abstract: 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 the associated variability. The vanilla GP however is hard to learn complicated distribution with the property of, e.g., heteroscedastic noise, multi-modality and non-stationarity, from massive data due to the Gaussian marginal and the cubic complexity. To this end, this article studies new scalable GP paradigms including the non-stationary heteroscedastic GP, the mixture of GPs and the latent GP, which introduce additional latent variables to modulate the outputs or inputs in order to learn richer, non-Gaussian statistical representation. Particularly, we resort to different variational inference strategies to arrive at analytical or tighter evidence lower bounds (ELBOs) of the marginal likelihood for efficient and effective model training. Extensive numerical experiments against state-of-the-art GP and neural network (NN) counterparts on various tasks verify the superiority of these scalable modulated GPs, especially the scalable latent GP, for learning diverse data distributions.
URI: https://hdl.handle.net/10356/162582
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2021.108121
Rights: © 2021 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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