Gaussian Process Regression and Classification under Mathematical Constraints with Learning Guarantees
Year: 2019
Type: preprint
Abstract: We introduce constrained Gaussian process (CGP), a Gaussian process model for random functions that allows easy placement of mathematical constrains (e.g., non-negativity, monotonicity, etc) on its sample functions. CGP comes with closed-form probability density function (PDF), and has the attractive feature that its posterior distributions for regression and classification are again CGPs with closed-form expressions. Furthermore, we show that CGP inherents the optimal theoretical properties of the Gaussian process, e.g. rates of posterior contraction, due to the fact that CGP is an Gaussian process with a more efficient model space.
Source: arXiv (Cornell University)
Author: Liu, Jeremiah Zhe
Institutions: Harvard University, Cancer Research And Biostatistics
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2938529511