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Random Search for Hyper-Parameter Optimization

Journal of Machine Learning Research 13 (2012) 281-305 Submitted 3/11; Revised 9/11; Published 2/12. Random Search for Hyper-Parameter Optimization James Bergstra JAMES . BERGSTRA @ UMONTREAL . CA. Yoshua Bengio YOSHUA . BENGIO @ UMONTREAL . CA. De partement d'Informatique et de recherche ope rationnelle Universite de Montre al Montre al, QC, H3C 3J7, Canada Editor: Leon Bottou Abstract Grid Search and manual Search are the most widely used strategies for Hyper-Parameter optimiza- tion. This paper shows empirically and theoretically that randomly chosen trials are more efficient for Hyper-Parameter Optimization than trials on a grid.

RANDOM SEARCH FOR HYPER-PARAMETER OPTIMIZATION search is used to identify regions in Λthat are promising and to develop the intuition necessary to choose the sets L(k).A major drawback of manual search is the difficulty in reproducing results.

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