I think finding one method is indeed the goal. Even if it is not the best
every time, a 90% solution for 10% of the complexity would be awesome. I
think GPs with parameter space warping are *probably* the best solution but
only a good implementation will show for sure.
Spearmint and hyperopt exist and work for more complex stuff but with far
more moving parts and complexity. Having a tool which is easy to use as the
grid search and random search modules currently are would be a big benefit.
My .02c
Kyle
On Mar 7, 2015 7:48 AM, "Christof Angermueller" <c.angermuel...@gmail.com>
wrote:
> Hi Andreas (and others),
>
> I am a PhD student in Bioinformatics at the University of Cambridge,
> (EBI/EMBL), supervised by Oliver Stegle and Zoubin Ghahramani. In my PhD, I
> apply and develop different machine learning algorithms for analyzing
> biological data.
>
> There are different approaches for hyperparameter optimization, some of
> which you mentioned on the topics page:
> * Sequential Model-Based Global Optimization (SMBO) ->
> http://www.cs.ubc.ca/labs/beta/Projects/SMAC/
> * Gaussian Processes (GP) -> Spearmint;
> https://github.com/JasperSnoek/spearmint
> * Tree-structured Parzen Estimator Approach (TPE) -> Hyperopt:
> http://hyperopt.github.io/hyperopt/
>
> And more recent approaches based on neural networks:
> * Deep Networks for Global Optimization (DNGO) ->
> http://arxiv.org/abs/1502.05700
>
> The idea is to implement ONE of this approaches, right?
>
> Do you prefer a particular approach due to theoretical or practical
> reasons?
>
> Spearmint also supports distributing jobs on a cluster (SGE). I imagine
> that this requires platform specific code, which could be difficult to
> maintain. What do you think?
>
> Spearmint and hyperopt are already established python packages. Another
> sklearn implementation might be considered as redundant, are hard to
> establish. Do you have a particular new feature in mind?
>
>
> Cheers,
> Christof
>
> --
> Christof Angermuellercangermueller@gmail.comhttp://cangermueller.com
>
>
>
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