Re: [MLlib] Gaussian Process regression in MLlib

[Valeriy Avanesov]

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 > 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.



-
To unsubscribe e-mail: dev-unsubscr...@spark.apache.org

Re: [MLlib] Gaussian Process regression in MLlib

[Аванесов Валерий]

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  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.
>>>
>>>
>>> -
>>> To unsubscribe e-mail: dev-unsubscr...@spark.apache.org
>>>
>>>
>>
>

Re: [MLlib] Gaussian Process regression in MLlib

[Valeriy Avanesov]

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.


-
To unsubscribe e-mail: dev-unsubscr...@spark.apache.org






-
To unsubscribe e-mail: dev-unsubscr...@spark.apache.org

Re: [MLlib] Gaussian Process regression in MLlib

[Simon Dirmeier]

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.


-
To unsubscribe e-mail: dev-unsubscr...@spark.apache.org




-
To unsubscribe e-mail: dev-unsubscr...@spark.apache.org