Non-Parametric Regression

Gaussian Process

Gaussian Processes are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for an unseen point from training data. The models are fully probabilistic so uncertainty bounds are baked in with the model.

Image Source: Gaussian Processes for Machine Learning, C. E. Rasmussen & C. K. I. Williams


Paper Code Results Date Stars


Task Papers Share
Bayesian Optimization 58 15.26%
GPR 44 11.58%
Active Learning 26 6.84%
Time Series Analysis 18 4.74%
Decision Making 16 4.21%
Classification 10 2.63%
BIG-bench Machine Learning 10 2.63%
Meta-Learning 8 2.11%
Dimensionality Reduction 7 1.84%


Component Type
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