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[D] – Bayesian Optimization with dynamic number of parameters

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?

submitted by /u/ktessera
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.