Random Search for Hyperparameters using Determinantal Point Processes

06/06/2017

∙

by   Jesse Dodge, et al.

∙

0

∙

share

We propose the use of k-determinantal point processes in hyperparameter optimization via random search. Compared to conventional approaches where hyperparameter settings are sampled independently, a k-DPP promotes diversity. We describe an approach that transforms hyperparameter search spaces for efficient use with a k-DPP. Our experiments show significant benefits over uniform random search in realistic scenarios with a limited budget for training supervised learners, whether in serial or parallel.