# Deep Neural Architecture Search with Deep Graph Bayesian Optimization

arXiv: https://arxiv.org/abs/1905.06159 · HTML: https://mlsanity.com/paper/1905.06159/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2019.

## Built on by (most cited first, 18 of 18)

- [Neural Architecture Search: Insights from 1000 Papers](https://mlsanity.com/paper/2301.08727/) (2023)
- [How Powerful are Performance Predictors in Neural Architecture Search?](https://mlsanity.com/paper/2104.01177/) (2021)
- [ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxies](https://mlsanity.com/paper/2110.10423/) (2021)
- [Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization](https://mlsanity.com/paper/2309.04810/) (2023)
- [Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization](https://mlsanity.com/paper/2206.14115/) (2022)
- [Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale](https://mlsanity.com/paper/2201.06834/) (2022)
- [A Survey on Computationally Efficient Neural Architecture Search](https://mlsanity.com/paper/2206.01520/) (2022)
- [Surprisingly Strong Performance Prediction with Neural Graph Features](https://mlsanity.com/paper/2404.16551/) (2024)
- [Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars](https://mlsanity.com/paper/2211.01842/) (2022)
- [A Novel Genetic Algorithm with Hierarchical Evaluation Strategy for Hyperparameter Optimisation of Graph Neural Networks](https://mlsanity.com/paper/2101.09300/) (2021)
- [Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search](https://mlsanity.com/paper/2109.07582/) (2021)
- [Graph Bayesian Optimization for Multiplex Influence Maximization](https://mlsanity.com/paper/2403.18866/) (2024)
- [Sample-Efficient Optimisation with Probabilistic Transformer Surrogates](https://mlsanity.com/paper/2205.13902/) (2022)
- [Bayesian Optimization and Deep Learning forsteering wheel angle prediction](https://mlsanity.com/paper/2110.13629/) (2021)
- [BioArc: Discovering Optimal Neural Architectures for Biological Foundation Models](https://mlsanity.com/paper/2512.00283/) (2025)
- [Efficient Automatic Machine Learning via Design Graphs](https://mlsanity.com/paper/2210.12257/) (2022)
- [ZAPS: Zero-Cost Active Proxy Search for Neural Architecture Search](https://mlsanity.com/paper/2609.14184/) (2026)
- [Bayesian Optimization in Sequence-to-Architecture Latent Space for Zero-Shot NAS](https://mlsanity.com/paper/2610.06167/) (2026)

## Abstract (arXiv)

Bayesian optimization (BO) is an effective method of finding the global optima of black-box functions. Recently BO has been applied to neural architecture search and shows better performance than pure evolutionary strategies. All these methods adopt Gaussian processes (GPs) as surrogate function, with the handcraft similarity metrics as input. In this work, we propose a Bayesian graph neural network as a new surrogate, which can automatically extract features from deep neural architectures, and use such learned features to fit and characterize black-box objectives and their uncertainty. Based on the new surrogate, we then develop a graph Bayesian optimization framework to address the challenging task of deep neural architecture search. Experiment results show our method significantly outperforms the comparative methods on benchmark tasks.