Hi Everyone,

I would like to use Bayesian Optimization to tune some hyperparameters. The problem is that the number of hyperparameters I want to tune is also a hyperparameter. These hyperparameters all relate to the same thing, i.e. all of them are learning rate, but I am not sure of the number of learning rate hyperparameters I need.

So I want to tune the number of hyperparameters n and tune n hyperparameters. I was thinking of justing having the max length of hyperparameters and then also have a parameter for the num of hyperparameters and selectiviely choosing only n parameters. Also since number of hyperparameters is an int, I know this is not ideal. Is there a more principled approach? Would I need to implement a specialized kernel function for this?