# Re: [MLlib] Gaussian Process regression in MLlib

Hi all,


please, check out the repo: github.com/akopich/spark-gp/. I've implemented the regressor.


Simon, have you still got smth to try it out on?

Best,

Valeriy.

On 02/15/2018 05:16 PM, Аванесов Валерий wrote:

Hi all,

I've created a new JIRA.

https://issues.apache.org/jira/browse/SPARK-23437

All concerned are welcome to discuss.

Best,
Valeriy.


On Sat, Feb 3, 2018 at 9:24 PM, Valeriy Avanesov <acop...@gmail.com <mailto:acop...@gmail.com>> wrote:

Hi,

no, I don't thing we should actually compute the n \times n
matrix. Leave alone inverting it. However, variational inference
is only one of the many sparse GP approaches. Another option could
be Bayesian Committee.

Best,

Valeriy.

On 02/02/2018 09:43 PM, Simon Dirmeier wrote:

Hey,

I wanted to see that for a long time, too. :) If you'd plan on
implementing this, I could contribute.
However, I am not too familiar with variational inference for
the GPs which is what you would need I guess.
Or do you think it is feasible to compute the full kernel for
the GP?

Cheers,
S

Am 01.02.18 um 20:01 schrieb Valeriy Avanesov:

Hi all,

it came to my surprise that there is no implementation of
Gaussian Process in Spark MLlib. The approach is widely
known, employed and scalable (its sparse versions). Is
there a good reason for that? Has it been discussed before?

If there is a need in this approach being a part of MLlib
I am eager to contribute.

Best,

Valeriy.

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