  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.

Machine Learning;Machine Learning