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Machine learning hyperparameter tuning via Bayesian optimization exploiting monotonicity

University of British Columbia

We propose an algorithm for a family of optimization problems where the objective can be decomposed as a sum of functions with monotonicity properties. The motivating problem is optimization of hyperparameters of machine learning algorithms where we argue that the objective, validation error, can be decomposed into two approximately monotonic functions of the hyperparameters, along with some theoretical justification. Our proposed algorithm adapts Bayesian optimization methods to incorporate monotonicity constraints. We illustrate the improvement in search efficiency for applications of hyperparameter tuning in machine learning on an artificial problem and Penn Machine Learning Benchmarks.

Text

2019-04-16

Attribution-NoDerivatives 4.0 International

http://hdl.handle.net/2429/69727

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nd/4.0/