There are definitely API questions that I also just discusses with Dan.
There are some thing that we could improve, but I think the solutions 
depend a lot on if we want to do kernel engineering or not.
My thinking was that this part is probably the most controversial one, 
so this is what I asked about.

I am not sure how useful our current implementation is. I have not met a 
person that uses it.
That might be caused by the interface and missing documentation more 
than by the fact that we don't have custom kernels, though.

I don't think we want to add a HODLR solver (hierarchical off-diagonal 
low-rank solver).
It you want that, you should probably install gorge ;)

What I think would be great to have is gradient based optimization of 
the kernel parameters, and a way to do grid-search
using data likelihood (not sure if that is currently supported).

For the API, the naming is somewhat non-standard, it is not super clear 
what the parameters mean, and it is also not super clear
whether the kernel-parameters will be optimized for a given parameters 
setting.




On 11/25/2014 12:28 PM, Gael Varoquaux wrote:
> On Tue, Nov 25, 2014 at 12:23:50PM -0500, Kyle Kastner wrote:
>> specifically a HODLR solver.
> What is this. Can you tell us more?
>
>> One very specific reason to focus on GP code quality would be that it
>> opens the door to use sklearn's own code to implement some very nice
>> hyperparameter search algorithms which could be useful to many users.
> Yes. I pretty badly would like this (Kyle, are you looking for an
> internship? You know, France is nice :P).
>
> Gaƫl
>
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