Valeriy Avanesov commented on SPARK-23437:

I've created a repo. https://github.com/akopich/spark-gp

> [ML] Distributed Gaussian Process Regression for MLlib
> ------------------------------------------------------
>                 Key: SPARK-23437
>                 URL: https://issues.apache.org/jira/browse/SPARK-23437
>             Project: Spark
>          Issue Type: New Feature
>          Components: ML, MLlib
>    Affects Versions: 2.2.1
>            Reporter: Valeriy Avanesov
>            Assignee: Apache Spark
>            Priority: Major
> Gaussian Process Regression (GP) is a well known black box non-linear 
> regression approach [1]. For years the approach remained inapplicable to 
> large samples due to its cubic computational complexity, however, more recent 
> techniques (Sparse GP) allowed for only linear complexity. The field 
> continues to attracts interest of the researches – several papers devoted to 
> GP were present on NIPS 2017. 
> Unfortunately, non-parametric regression techniques coming with mllib are 
> restricted to tree-based approaches.
> I propose to create and include an implementation (which I am going to work 
> on) of so-called robust Bayesian Committee Machine proposed and investigated 
> in [2].
> [1] Carl Edward Rasmussen and Christopher K. I. Williams. 2005. _Gaussian 
> Processes for Machine Learning (Adaptive Computation and Machine Learning)_. 
> The MIT Press.
> [2] Marc Peter Deisenroth and Jun Wei Ng. 2015. Distributed Gaussian 
> processes. In _Proceedings of the 32nd International Conference on 
> International Conference on Machine Learning - Volume 37_ (ICML'15), Francis 
> Bach and David Blei (Eds.), Vol. 37. JMLR.org 1481-1490.

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