Re: Efficient Parameter Tuning Software

Hi Chaz,

Thanks,  for handling continuous parameters
the approach we have in mind is to sample continuous values
but then do some adaptive binning to reason about specific
combinations of them – there are some details to be worked
out though.

Simon


From: "Chaz G." <[email protected]>
Date: Monday, 14 January 2019 at 19:20
To: "[email protected]" <[email protected]>
Cc: Simon Lucas <[email protected]>
Subject: Re: [Computer-go] Efficient Parameter Tuning Software

Hi Simon,

Thanks for sharing. In my opinion, apart from discretizing the search space, the N-Tuple system takes a very intuitive approach to hyper-parameter optimization. The github repo readme notes you're working on an extended version to handle continuous parameters, what's your general approach to that issue?

Thanks,
-Chaz

On Sun, Jan 13, 2019 at 11:51 AM Simon Lucas <[email protected]<mailto:[email protected]>> wrote:
Hi all,

The N-Tuple Bandit Evolutionary Algorithm aims
to provide sample-efficient optimisation, especially
for noisy problems.

Software available in Java and Python:

https://github.com/SimonLucas/ntbea

It also provides stats on the value of each parameter setting
and combinations of settings.

Best wishes,

Simon


--
Simon Lucas
Professor of Artificial Intelligence
Head of School
Electronic Engineering and Computer Science
Queen Mary University of London



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