  Hyperparameter optimization is an important subfield of machine learning that
focuses on tuning the hyperparameters of a chosen algorithm to achieve peak
performance. Recently, there has been a stream of methods that tackle the issue
of hyperparameter optimization, however, most of the methods do not exploit the
dominant power law nature of learning curves for Bayesian optimization. In this
work, we propose Deep Power Laws (DPL), an ensemble of neural network models
conditioned to yield predictions that follow a power-law scaling pattern. Our
method dynamically decides which configurations to pause and train
incrementally by making use of gray-box evaluations. We compare our method
against 7 state-of-the-art competitors on 3 benchmarks related to tabular,
image, and NLP datasets covering 59 diverse tasks. Our method achieves the best
results across all benchmarks by obtaining the best any-time results compared
to all competitors.
