Freeze-Thaw Bayesian Optimization

TL;DR

This paper introduces a dynamic Bayesian optimization method that leverages partial training information to efficiently select hyperparameters by deciding when to pause, resume, or restart training, improving hyperparameter tuning speed.

Contribution

It develops a novel covariance kernel and Gaussian process prior tailored for training curves, along with an information-theoretic framework for decision-making in hyperparameter optimization.

Findings

Effective in practice across multiple models

Reduces training time for hyperparameter tuning

Automates decision process with information theory

Abstract

In this paper we develop a dynamic form of Bayesian optimization for machine learning models with the goal of rapidly finding good hyperparameter settings. Our method uses the partial information gained during the training of a machine learning model in order to decide whether to pause training and start a new model, or resume the training of a previously-considered model. We specifically tailor our method to machine learning problems by developing a novel positive-definite covariance kernel to capture a variety of training curves. Furthermore, we develop a Gaussian process prior that scales gracefully with additional temporal observations. Finally, we provide an information-theoretic framework to automate the decision process. Experiments on several common machine learning models show that our approach is extremely effective in practice.