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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