ebjaime / local-and-global-sparse-gaussian-process-regression

Study of Gaussian Process (GP) local and global approximations, and application of the sparse GP approximation, combining both the global and local approaches.

Study of Gaussian Process (GP) local and global approximations

Gaussian process (GP) models are flexible probabilistic nonparametric models for regression, classification and other tasks. Unfortunately they suffer from computational intractability for large data sets. Over the past decade there have been many different approximations developed to reduce this cost. Most of these can be termed global approximations, in that they try to summarize all the training data via a small set of support points. A different approach is that of local regression, where many local experts account for their own part of space. In this project we are interested to study the regimes in which these different approaches work well or fail, and then apply a new sparse GP approximation which is a combination of both the global and local approaches, and look extremely promising.

The was modified and is so added to this repository for the proper execution of the programs. All the modifications made are tagged with a REVIEW tag.

[CST @ Polimi - 2021/22]