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Posted to issues@spark.apache.org by "Simon Dirmeier (JIRA)" <ji...@apache.org> on 2018/02/15 17:02:00 UTC
[jira] [Commented] (SPARK-23437) [ML] Distributed Gaussian Process
Regression for MLlib
[ https://issues.apache.org/jira/browse/SPARK-23437?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16365929#comment-16365929 ]
Simon Dirmeier commented on SPARK-23437:
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Great suggestion. If there is a way to contribute I'd love to.
> [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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