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c-TPE: Generalizing Tree-structured Parzen Estimator with Inequality Constraints for Continuous and Categorical Hyperparameter Optimization
Shuhei Watanabe · Frank Hutter

Hyperparameter optimization (HPO) is crucial for strong performance of deep learning algorithms. A widely-used versatile HPO method is a variant of Bayesian optimization called tree-structured Parzen estimator (TPE), which splits data into good and bad groups and uses the density ratio of those groups as an acquisition function (AF). However, real-world applications often have some constraints, such as memory requirements, or latency. In this paper, we present an extension of TPE to constraint optimization (c-TPE) via simple factorization of AFs. The experiments demonstrate c-TPE is robust to various constraint levels and exhibits the best average rank performance among existing methods with statistical significance on search spaces with categorical parameters on 81 settings.

Author Information

Shuhei Watanabe (The University of Freiburg)
Frank Hutter (University of Freiburg & Bosch)

Frank Hutter is a Full Professor for Machine Learning at the Computer Science Department of the University of Freiburg (Germany), where he previously was an assistant professor 2013-2017. Before that, he was at the University of British Columbia (UBC) for eight years, for his PhD and postdoc. Frank's main research interests lie in machine learning, artificial intelligence and automated algorithm design. For his 2009 PhD thesis on algorithm configuration, he received the CAIAC doctoral dissertation award for the best thesis in AI in Canada that year, and with his coauthors, he received several best paper awards and prizes in international competitions on machine learning, SAT solving, and AI planning. Since 2016 he holds an ERC Starting Grant for a project on automating deep learning based on Bayesian optimization, Bayesian neural networks, and deep reinforcement learning.

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