Open Loop Hyperparameter Optimization and Determinantal Point Processes
Authors: Jesse Dodge, Kevin Jamieson, Noah Smith
Keywords: hyperparameter optimization, black box optimization
TL;DR: We address fully parallel hyperparameter optimization with Determinantal Point Processes. 
Abstract: Driven by the need for parallelizable hyperparameter optimization methods, this paper studies open loop search methods: sequences that are predetermined and can be generated before a single configuration is evaluated. Examples include grid search, uniform random search, low discrepancy sequences, and other sampling distributions.
In particular, we propose the use of k-determinantal point processes in  hyperparameter optimization via random search. Compared to conventional uniform random search 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. In addition, we introduce a novel Metropolis-Hastings algorithm which can sample from k-DPPs defined over any space from which uniform samples can be drawn, including spaces with a mixture of discrete and continuous dimensions or tree structure. Our experiments show significant benefits  in realistic scenarios with a limited budget for training supervised learners, whether in serial or parallel.