Hyperparameter Optimization Machines
2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)(2016)
摘要
Algorithm selection and hyperparameter tuning are omnipresent problems for researchers and practitioners. Hence, it is not surprising that the efforts in automatizing this process using various meta-learning approaches have been increased. Sequential model-based optimization (SMBO) is ne of the most popular frameworks for finding optimal hyperparameter configurations. Originally designed for black-box optimization, researchers have contributed different meta-learning approaches to speed up the optimization process. We create a generalized framework of SMBO and its recent additions which gives access to adaptive hyperparameter transfer learning with simple surrogates (AHT), a new class of hyperparameter optimization strategies. AHT provides less time-overhead for the optimization process by replacing time-and space-consuming transfer surrogate models with simple surrogates that employ adaptive transfer learning. In an empirical comparison on two different meta-data sets, we can show that AHT outperforms various instances of the SMBO framework in the scenarios of hyperparameter tuning and algorithm selection.
更多查看译文
关键词
hyperparameter optimization,meta-learning,transfer learning
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要