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Algorithms for Hyper-Parameter Optimization

Algorithms for Hyper-Parameter Optimization James Bergstra Re mi Bardenet The Rowland Institute Laboratoire de Recherche en Informatique Harvard University Universite Paris-Sud Yoshua Bengio Bala zs Ke gl De pt. d'Informatique et Recherche Ope rationelle Linear Accelerator Laboratory Universite de Montre al Universite Paris-Sud, CNRS. Abstract Several recent advances to the state of the art in image classification benchmarks have come from better configurations of existing techniques rather than novel ap- proaches to feature learning. Traditionally, Hyper-Parameter Optimization has been the job of humans because they can be very efficient in regimes where only a few trials are possible. Presently, computer clusters and GPU processors make it pos- sible to run more trials and we show that algorithmic approaches can find better results.

Anticipating that our hyper-parameter optimization tasks will mean high dimensions and small fit-ness evaluation budgets, we now turn to another modeling strategy and EI optimization scheme for the SMBO algorithm. Whereas the Gaussian-process based approach modeled p(yjx) directly, this strategy models p(xjy) and p(y).

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