fernando

Of course there is 🤦 . When implementing it I didn't even think about it, but I agree, not re-inventing the wheel is definitely the way to go. If you...

Within the library all sources of random behaviour are deferred to an instance of `np.random.RandomState`. This is used for random number generation when needed, but is also passed as a...

After `np.random.RandomState` was added everywhere I imagined this would not be a problem, but never really tested it, to be honest.

To my knowledge the package is setting a seed to all possible sources of randomness that allow for seed control. I suspect [`scipy.minimize`](https://github.com/fmfn/BayesianOptimization/blob/master/bayes_opt/util.py#L55) might be the source of problem here,...

Ahh, I've seen this (and promptly ignored it) in the past. I believe it is a combination of UCB's obsession with edges, the bounded optimization of the acquisition function that...

Scratch that! This might be due to the function value being negative ==> UCB > 0 is not always true which seems to lead to trouble (not sure why yet,...

There's a bug with the proxy optimization done on the acquisition function, which is particularly severe with expected improvement. I'm not sure what is causing it yet, unfortunately, but the...

Gaussian processes can naturally handle noisy regression (when the labels have noise); instead of assigning zero posterior variance to observed points, noisy GP with still show some uncertainty. The noise...

What kappa value are you using? 11?

That's fine. I suspect the reason why what you see plotted doesn't match what is actually chosen is likely due to you plotting the 95% C.I., while exploring with a...