Bayesian Optimization for Selecting Efficient Machine Learning Models
This addresses the need for efficient model selection in real-world applications where training time constraints are critical, though it is incremental as it extends existing Bayesian Optimization approaches.
The paper tackles the problem of hyper-parameter optimization in machine learning by proposing a Bayesian Optimization framework that jointly optimizes for both prediction effectiveness and training efficiency, with experiments on recommendation tasks showing significant improvements in training efficiency while maintaining strong effectiveness compared to state-of-the-art methods.
The performance of many machine learning models depends on their hyper-parameter settings. Bayesian Optimization has become a successful tool for hyper-parameter optimization of machine learning algorithms, which aims to identify optimal hyper-parameters during an iterative sequential process. However, most of the Bayesian Optimization algorithms are designed to select models for effectiveness only and ignore the important issue of model training efficiency. Given that both model effectiveness and training time are important for real-world applications, models selected for effectiveness may not meet the strict training time requirements necessary to deploy in a production environment. In this work, we present a unified Bayesian Optimization framework for jointly optimizing models for both prediction effectiveness and training efficiency. We propose an objective that captures the tradeoff between these two metrics and demonstrate how we can jointly optimize them in a principled Bayesian Optimization framework. Experiments on model selection for recommendation tasks indicate models selected this way significantly improves model training efficiency while maintaining strong effectiveness as compared to state-of-the-art Bayesian Optimization algorithms.