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https://issues.apache.org/jira/browse/SPARK-23437?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16366744#comment-16366744
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Nick Pentreath commented on SPARK-23437:
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It sounds interesting - however the standard practice is that new algorithms
should probably be released as a 3rd party Spark package. If they become
widely-used then there is a stronger argument for integration into MLlib.
See [http://spark.apache.org/contributing.html] under the MLlib section for
more details.
> [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
> 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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