There are a lot of ways to speed them up as potential work, but the
interface (and backend code) should be very stable first. Gradient based,
latent variable approximation, low-rank updating, and distributed GP (new
paper from a few weeks ago) are all possible, but would need to be compared
to a very solid implementation with stable API to determine how bad
approximation is and if it is worth it performance wise. But if we
eventually want some form of Bayesian hyperparameter optimization a very
stable and fast-ish GP is likely necessary, with way more emphasis on the
*stable* part. So basically, what Andy said :)
Also I am +1 to Alex's list. Seems like it covers a lot of stuff that needs
TLC
Kyle
On Thu, Feb 12, 2015 at 5:10 PM, Andy <t3k...@gmail.com> wrote:
> Sorry, I was using a possibly confusing idiom. The problem with our GP is
> not so much speed as interface and flexibility.
> Also, we are not using gradient based parameter optimization.
>
>
> On 02/12/2015 05:48 PM, Artem wrote:
>
> Do you have any particular ideas on how one could speedup GPs, besides
> reimplementing it in Cython? Looks like spearmint is completely pythonic,
> so they either as slow (or slower), or use different algorithm (I'm not
> very familiar with approaches to GPs).
>
> On Fri, Feb 13, 2015 at 12:41 AM, Andy <t3k...@gmail.com> wrote:
>
>>
>> On 02/12/2015 04:47 AM, Artem wrote:
>>
>> There are several packages (spearmint, hyperopt, MOE) offering Bayesian
>> Optimization to the problem of choosing hyperparameters. Wouldn't it be
>> nice to add such *Search[CV] to sklearn?
>>
>> Yes. I haven't really looked much into the spearmint approach, but
>> before we could do anything with GPs I am afraid we need to get our GP up
>> to speed.
>>
>>
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