[Computer-go] CLOP: Confident Local Optimization forNoisyBlack-Box Parameter Tuning

Rémi Coulom Remi.Coulom at free.fr
Thu Oct 6 00:55:05 PDT 2011

On 4 oct. 2011, at 23:58, Brian Sheppard wrote:

> My implementation is missing the Gaussian prior. That seems to explain all
> of the issues.

Yes, having a prior is very important. In the draft of my paper, I wrote that its choice has little influence on performance of the algorithm. It is correct for low-dimensional problems (ie n < 10), but wrong when the dimension is high. Regularization should be stronger in high dimensions. Maybe using sparse logistic regression is a good idea in high dimensions. I'll research this question more when I have time.

> It is especially important that having the prior will focus attention on the
> region of success. In the case of Correlated2, where only a tiny fraction of
> the space is non-zero, that will massively reduce the burn-in period.

Note that CLOP as described in my paper requires no explicit logic for a burn-in period. You can run that CLOP procedure with any number of samples, from zero to ten millions.


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