spark-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Valeriy Avanesov (JIRA)" <>
Subject [jira] [Updated] (SPARK-23437) [ML] Distributed Gaussian Process Regression for MLlib
Date Thu, 15 Feb 2018 16:13:01 GMT


Valeriy Avanesov updated SPARK-23437:
    Summary: [ML] Distributed Gaussian Process Regression for MLlib  (was: Distributed Gaussian
Process Regression for MLlib)

> [ML] Distributed Gaussian Process Regression for MLlib
> ------------------------------------------------------
>                 Key: SPARK-23437
>                 URL:
>             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. 1481-1490.

This message was sent by Atlassian JIRA

To unsubscribe, e-mail:
For additional commands, e-mail:

View raw message