MOFA: Modular Factorial Design for Hyperparameter Optimization
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Automated hyperparameter optimization (HPO) has shown great power in many machine learning applications. While existing methods suffer from model selection, parallelism, or sample efficiency, this paper presents a new HPO method, MOdular FActorial Design (MOFA), to address these issues simultaneously. The major idea is to use techniques from Experimental Designs to improve sample efficiency of model-free methods. Particularly, MOFA runs with four modules in each iteration: (1) an Orthogonal Latin Hypercube (OLH)-based sampler preserving both univariate projection uniformity and orthogonality; (2) a highly parallelized evaluator; (3) a transformer to collapse the OLH performance table into a specified Fractional Factorial Design–Orthogonal Array (OA); (4) an analyzer including Factorial Performance Analysis and Factorial Importance Analysis to narrow down the search space. We theoretically and empirically show that MOFA has great advantages over existing model-based and model-free methods.
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