In this paper, we propose a novel grid spectral mixture (GSM) kernel design for GPs that can automatically fit multidimensional data with affordable model ...
The proposed algorithm alleviates the computational complexity caused by the curse of dimensionality in the multidimensional input case by leveraging a ...
This repository contains the code and data used in the paper "Gaussian Process Regression with Grid Spectral Mixture Kernel: Distributed Learning for ...
In the next cell, we plot the mean and confidence region of the Gaussian process model. The confidence_region method is a helper method that returns 2 standard ...
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We present a scalable Gaussian process model for identifying and characterizing smooth multidimensional changepoints, and automatically learning changes in ...
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The newly proposed grid spectral mixture (GSM) kernel is tailored for multi-dimensional data, effectively reducing the number of hyper-parameters while ...
Nov 29, 2020 · Hi everyone. I've been doing some messing around with the spectral mixture kernel for GP regression. I've tried to implement this kernel in ...
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Gaussian Process Regression with Grid Spectral Mixture Kernel: Distributed Learning for Multidimensional Data. Conference Paper. Jul 2022.
We model a spectral density as a transformed Gaussian process, providing a non-parametric function-space distribution over kernels. Our approach, functional ...
In this paper, we propose a novel GPR model, where multi-dimensional sample inputs are viewed as signals generated over an underlying graph.