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arXiv · 1904.11095

Reducing The Search Space For Hyperparameter Optimization Using Group Sparsity

Minsu Cho·Chinmay Hegde
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Abstract

We propose a new algorithm for hyperparameter selection in machine learning algorithms. The algorithm is a novel modification of Harmonica, a spectral hyperparameter selection approach using sparse recovery methods. In particular, we show that a special encoding of hyperparameter space enables a natural group-sparse recovery formulation, which when coupled with HyperBand (a multi-armed bandit strategy) leads to improvement over existing hyperparameter optimization methods such as Successive Halving and Random Search. Experimental results on image datasets such as CIFAR-10 confirm the benefits of our approach.

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Minsu Cho, Chinmay Hegde. 2019-04-24. Reducing The Search Space For Hyperparameter Optimization Using Group Sparsity. https://arxiv.org/abs/1904.11095

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