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From Valeriy Avanesov <acop...@gmail.com>
Subject Re: [MLlib] Gaussian Process regression in MLlib
Date Mon, 12 Mar 2018 10:11:37 GMT
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 <acopich@gmail.com
> <mailto:acopich@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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