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:
* Scalable Bayesian Optimization Using Deep 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 Angermueller
cangermuel...@gmail.com
http://cangermueller.com
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