Keywords: Bayesian Optimization, AutoML, Hyperparameter Optimization, Neural Architecture Search
Abstract: We introduce a model-based asynchronous multi-fidelity method for hyperparameter and neural architecture search that combines the strengths of asynchronous Successive Halving 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.
One-sentence Summary: We present a new, asynchronous multi-fidelty Bayesian optimization method to efficiently search for hyperparameters and architectures of neural networks.
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