Model-based Asynchronous Hyperparameter Optimization

by   Louis C. Tiao, et al.

We introduce a model-based asynchronous multi-fidelity hyperparameter optimization (HPO) method, combining strengths of asynchronous Hyperband and Gaussian process-based Bayesian optimization. Our method obtains substantial speed-ups in wall-clock time over, both, synchronous and asynchronous Hyperband, as well as a prior model-based extension of the former. Candidate hyperparameters to evaluate are selected by a novel jointly dependent Gaussian process-based surrogate model over all resource levels, allowing evaluations at one level to be informed by evaluations gathered at all others. We benchmark several covariance functions and conduct extensive experiments on hyperparameter tuning for multi-layer perceptrons on tabular data, convolutional networks on image classification, and recurrent networks on language modelling, demonstrating the benefits of our approach.


page 1

page 2

page 3

page 4


pySOT and POAP: An event-driven asynchronous framework for surrogate optimization

This paper describes Plumbing for Optimization with Asynchronous Paralle...

Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation

Batch Bayesian optimisation (BO) has been successfully applied to hyperp...

Towards Learning Universal Hyperparameter Optimizers with Transformers

Meta-learning hyperparameter optimization (HPO) algorithms from prior ex...

HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization

We present a new software, HYPPO, that enables the automatic tuning of h...

PARyOpt: A software for Parallel Asynchronous Remote Bayesian Optimization

PARyOpt is a python based implementation of the Bayesian optimization ro...

Multi-level Training and Bayesian Optimization for Economical Hyperparameter Optimization

Hyperparameters play a critical role in the performances of many machine...

Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimization

This paper investigates lung nodule classification by using deep neural ...