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Related Experiment Video

Updated: Oct 23, 2025

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

Splitting Gaussian processes for computationally-efficient regression.

Nick Terry¹, Youngjun Choe¹

¹Department of Industrial and Systems Engineering, University of Washington, Seattle, WA, United States of America.

|August 24, 2021

Summary

This study introduces a novel localized Gaussian process regression algorithm. It efficiently handles large datasets by partitioning input space, offering superior time and space complexity for scalable machine learning.

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Last Updated: Oct 23, 2025

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Setting Limits on Supersymmetry Using Simplified Models

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

Area of Science:

Machine Learning
Statistical Modeling

Background:

Gaussian processes (GPs) are powerful kernel methods for regression.
Standard GPs exhibit cubic time complexity, limiting scalability with large datasets.
Efficient updating of GP models is crucial for practical applications.

Purpose of the Study:

To develop a scalable Gaussian process regression algorithm.
To address the computational limitations of standard GP models.
To introduce a localized Gaussian process regression model with improved efficiency.

Main Methods:

Propose an algorithm for sequential input space partitioning.
Fit localized Gaussian processes to disjoint regions.
Achieve a model with tightly bounded update time complexity.

Main Results:

The algorithm demonstrates superior time and space complexity compared to existing methods.
The localized GP model achieves linear memory complexity.
Theoretical continuity properties of the model are established.

Conclusions:

The proposed localized Gaussian process regression model offers significant scalability improvements.
The algorithm enables efficient model updating with bounded time complexity.
The model's efficacy is validated on multi-dimensional regression tasks.