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
<mailto: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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