# Model-based Asynchronous Hyperparameter and Neural Architecture Search

Source: [Model-based Asynchronous Hyperparameter and Neural Architecture Search](<https://www.sota2.com/research/paper/68fa67d61e209131cea18d01>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:02:46.151Z

Authors: Aaron Klein, Louis C. Tiao, Thibaut Lienart, Cedric Archambeau, Matthias Seeger

Published: 2020-03-24T14:17:07.000Z

## Abstract

We introduce a model-based asynchronous multi-fidelity method for hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian process-based Bayesian optimization. At the heart of our method is a probabilistic model that can simultaneously reason across hyperparameters and resource levels, and supports decision-making in the presence of pending evaluations. We demonstrate the effectiveness of our method on a wide range of challenging benchmarks, for tabular data, image classification and language modelling, and report substantial speed-ups over current state-of-the-art methods. Our new methods, along with asynchronous baselines, are implemented in a distributed framework which will be open sourced along with this publication.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2003.10865>)

[PDF](<https://arxiv.org/pdf/2003.10865.pdf>)