Neural Architecture Search with Bayesian Optimisation and Optimal Transport

TL;DR

This paper introduces NASBOT, a Bayesian optimisation framework using Gaussian processes and optimal transport to efficiently search for neural network architectures, outperforming existing methods in model selection tasks.

Contribution

The paper develops NASBOT, a novel Bayesian optimisation method for neural architecture search that employs an optimal transport-based distance metric for architectures.

Findings

NASBOT outperforms other architecture search methods in several tasks.

The optimal transport-based distance metric is computed efficiently.

The method is effective for both MLPs and CNNs.

Abstract

Bayesian Optimisation (BO) refers to a class of methods for global optimisation of a function $f$ which is only accessible via point evaluations. It is typically used in settings where $f$ is expensive to evaluate. A common use case for BO in machine learning is model selection, where it is not possible to analytically model the generalisation performance of a statistical model, and we resort to noisy and expensive training and validation procedures to choose the best model. Conventional BO methods have focused on Euclidean and categorical domains, which, in the context of model selection, only permits tuning scalar hyper-parameters of machine learning algorithms. However, with the surge of interest in deep learning, there is an increasing demand to tune neural network \emph{architectures}. In this work, we develop NASBOT, a Gaussian process based BO framework for neural architecture search. To accomplish this, we develop a distance metric in the space of neural network architectures which can be computed efficiently via an optimal transport program. This distance might be of independent interest to the deep learning community as it may find applications outside of BO. We demonstrate that NASBOT outperforms other alternatives for architecture search in several cross validation based model selection tasks on multi-layer perceptrons and convolutional neural networks.