-
I found the `optimize_restarts` feature from GPy to be particularly useful for getting really good fits especially in the way I am working with GPs. I also noticed that there was work done for this ba…
-
### Description
Most (if not all) non-auto models contain `random_seed`, but this option seems missing for `Auto*` models. It will be very useful to have an option exposed so that we can ensure every…
-
Hello!
I like the way you implemented the decreasing in mutation probability to balance exploitation and exploration and the hyperparameter grid search
The first thing I would try to address is t…
-
hyperparameter optimization & some manual analysis
-
Hi,
I believe that higher could be used for hyperparameter optimization using bilevel programming. I have attempted to adapt the given meta-learning example for bilevel programming. However, I am s…
-
Hey Jordan, looking at problem 2.4, how do you want us to implement the neural network? Do you want us to use:
**Method #1**
model = Sequential()
model.add(Dense(256, activation='relu',input_shap…
-
Hello,
I am using surrogates.jl v6.3.0 to build kriging for a dataset with 500 elements. There are 24 input variables and 1 output. But I found the accuracy is terrible. There is serious overfittin…
-
Setting a strong beta-prior that encourages short lengthscales (e.g., `Gamma(0.25, 0.5)`) can lead to numerical issues during hyperparameter optimization. In particular, `optimize_restarts` samples hy…
-
Is it possible to get the expected minimum numerically from `.plot_objective()` rather than `.expected_minimum()`?
The following code gives me a different expected minimum than what the graph shows…
-
I’m using tsfresh to generate tabular data from my time series. I have 3 channels per time series, and it generates 775 features each, so I have 2325 features total.
Fitting an EBM on my dataset (3…