Large-Scale Heteroscedastic Regression via Gaussian Process

Volume: 32, Issue: 2, Pages: 708 - 721
Published: Feb 1, 2021
Abstract
Heteroscedastic regression considering the varying noises among observations has many applications in the fields, such as machine learning and statistics. Here, we focus on the heteroscedastic Gaussian process (HGP) regression that integrates the latent function and the noise function in a unified nonparametric Bayesian framework. Though showing remarkable performance, HGP suffers from the cubic time complexity, which strictly limits its...
Paper Details
Title
Large-Scale Heteroscedastic Regression via Gaussian Process
Published Date
Feb 1, 2021
Volume
32
Issue
2
Pages
708 - 721
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