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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.

figuration spaces are tree-structured in the sense that some leaf variables (e.g. the number of hidden units in the 2nd layer of a DBN) are only well-defined when node variables (e.g. a discrete choice of ... In this work we define a configuration space by a generative process for drawing valid samples.

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