Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels

Abstract

Current neural architecture search (NAS) strategies focus only on finding a single, good, architecture. They offer little insight into why a specific network is performing well, or how we should modify the architecture if we want further improvements. We propose a Bayesian optimisation (BO) approach for NAS that combines the Weisfeiler-Lehman graph kernel with a Gaussian process surrogate. Our method not only optimises the architecture in a highly data-efficient manner, but also affords interpretability by discovering useful network features and their corresponding impact on the network performance. Moreover, our method is capable of capturing the topological structures of the architectures and is scalable to large graphs, thus making the high-dimensional and graph-like search spaces amenable to BO. We demonstrate empirically that our surrogate model is capable of identifying useful motifs which can guide the generation of new architectures. We finally show that our method outperforms existing NAS approaches to achieve the state of the art on both closed- and open-domain search spaces.

Publication
Proceedings of the 9th International Conference on Learning Representations

This work was also presented at the AutoML seminar on 3 Dec 2020.

Xingchen Wan
Xingchen Wan
Research Scientist

My research interests include Bayesian optimization, AutoML, graphs & large language models.