i want to know whether autosklearn support multilabel task, thank you!

The docstring in the gaussian_ei acquisition function mentions

Note that the value returned by this function should be maximized to
obtain the X with maximum improvement.

However, in the Optimizer class, we see that the next x is determined by the following code for the 'sampling' acquisition optimizer.
` values = _gaussian_acquisition(
X=X, model=est, y_op

Expected Results

List of cost of all configs, or run if more then one run per config

Actual Results

Don't know where it is

Versions

0.11.1

The download buttons for the examples on readthedocs are not working. I cloned the repo and built the docs locally and it works fine. I guess just something in the build process went wrong and it should be working when you build it again.

This issue that surfaced from conversation with @henrymoss .

If you wanted to do parallel evaluations of your objective, you might be able to do that with Emukit, but it isn't obvious how to get started. A good tutorial would be nice to help people out.

The current documentation for the callback feature is not sufficient. Its a powerful tool but today I noticed the current documentation fails to cover the cases for which its useful.

Hi,

Thanks for sharing the code.

Is there ant simple "getting started" document on setting up like a basic EI or KG with your tool.
1- I have seen the main.py in the "example" folder, it is good but it is for a very complete case, not for simple sampling mode.
2- I have tried the MOE getting started document (available in the "doc" folder) but as the function names and procedures are chan

The documentation mentions that mlrMBO supports conditional parameters, but I can't find an example. Could you provide a very simple example?

https://papers.nips.cc/paper/7818-adversarially-robust-optimization-with-gaussian-processes.pdf

Lot of people state that Appveyor is better than travis and now they have linux support.. Maybe at some point we want to change to Appveyor..

how to describe xgb parameters inlcude booster subparameters using space.py,
mycode

{
           "min_child_weight": choco.uniform(0, 20),
           "n_estimators": choco.quantized_uniform(25, 525, 20),
           "max_depth": choco.quantized_uniform(1, 20, 1),
           "subsample": choco.uniform(0.7, 1.0),
           "learning_rate": choco.uniform(0.001, 1.0),
         

From Lu Cheng:

"It was said in GPStuff manual page 42 that periodic kernel was coming
from this paper
http://jmlr.org/proceedings/papers/v33/solin14.pdf

In page 907, equation (23) and GPStuff appendix, there is the canonical
periodic covariance function. And it is not obvious to find the explicit
form of quasi-periodic covariance function in section 3.5.

In the demo_periodic.m, there is alway t