[5d749d7da7d5]

This looks super interesting, I have previously considered using the Bayesian Optimization[0] package for some work, but the ability to switch out the underlying algorithms is appealing.

Perhaps a bit of a far out question - I would be interested in using this for optimizing real-world (ie slow, expensive, noisy) processes. A caveat with this is that the work is done in batches (eg N experiments at a time). Is there a mechanism by which I could feed in my results from previous rounds and have the algorithm suggest the next N configurations that are sufficiently uncorrelated to explore promising space without bunching on top of each-other? My immediate read is that I could use the package to pick the next optimal point, but would then have to lean on a random search for the remainder of the batch?

0: https://github.com/fmfn/BayesianOptimization

[simonblanke]

Yes it is quite easy to switch algorithms via the "gpr" parameter. You just have to write a wrapper class. I am currently working on a repository that discusses how to do that in detail: https://github.com/SimonBlanke/surrogate-models I will upload some examples (GPy and Gpflow) within the next 24 hours.

I think those wrapper-classes will already answer some of your questions. If you have additional or unanswered questions you can open an issue in the Grad-Free-Opt repository.