Transductive Log Opinion Pool of Gaussian Process Experts

11/24/2015
by   Yanshuai Cao, et al.
0

We introduce a framework for analyzing transductive combination of Gaussian process (GP) experts, where independently trained GP experts are combined in a way that depends on test point location, in order to scale GPs to big data. The framework provides some theoretical justification for the generalized product of GP experts (gPoE-GP) which was previously shown to work well in practice but lacks theoretical basis. Based on the proposed framework, an improvement over gPoE-GP is introduced and empirically validated.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/28/2014

Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions

In this work, we propose a generalized product of experts (gPoE) framewo...
research
05/30/2019

Enriched Mixtures of Gaussian Process Experts

Mixtures of experts probabilistically divide the input space into region...
research
05/17/2023

A Global-Local Approximation Framework for Large-Scale Gaussian Process Modeling

In this work, we propose a novel framework for large-scale Gaussian proc...
research
03/23/2017

Distribution of Gaussian Process Arc Lengths

We present the first treatment of the arc length of the Gaussian Process...
research
02/12/2020

Ensemble of Sparse Gaussian Process Experts for Implicit Surface Mapping with Streaming Data

Creating maps is an essential task in robotics and provides the basis fo...
research
09/28/2022

Strain energy density as a Gaussian process and its utilization in stochastic finite element analysis: application to planar soft tissues

Data-based approaches are promising alternatives to the traditional anal...
research
12/17/2021

Correlated Product of Experts for Sparse Gaussian Process Regression

Gaussian processes (GPs) are an important tool in machine learning and s...

Please sign up or login with your details

Forgot password? Click here to reset