Lifelong Bayesian Optimization
This work addresses the challenge of scalable and efficient model selection in Auto-ML for data science applications, though it appears incremental as it builds on existing Bayesian optimization techniques.
The paper tackles the problem of model selection for datasets arriving over time by introducing Lifelong Bayesian Optimization (LBO), an online multitask algorithm that leverages past optimizations to speed up learning, and experiments show it outperforms standard Bayesian optimization methods.
Automatic Machine Learning (Auto-ML) systems tackle the problem of automating the design of prediction models or pipelines for data science. In this paper, we present Lifelong Bayesian Optimization (LBO), an online, multitask Bayesian optimization (BO) algorithm designed to solve the problem of model selection for datasets arriving and evolving over time. To be suitable for "lifelong" Bayesian Optimization, an algorithm needs to scale with the ever increasing number of acquisitions and should be able to leverage past optimizations in learning the current best model. We cast the problem of model selection as a black-box function optimization problem. In LBO, we exploit the correlation between functions by using components of previously learned functions to speed up the learning process for newly arriving datasets. Experiments on real and synthetic data show that LBO outperforms standard BO algorithms applied repeatedly on the data.