Abstract
Bayesian optimisation offers an efficient solution to optimise black box functions. When coupled with transfer learning methods, Bayesian optimisation can leverage data from other function optimisations. A crucial requirement of transfer learning, however, is to restrict the transfer of knowledge only from related functions. Since the relatedness is not known a priori, selection of useful sources is an important problem. To address this problem, we propose a new method for optimal source selection for transfer learning in Bayesian optimisation. Using multi-armed bandits for source selection, we construct a new technique for identifying the optimal source and then use it for transfer learning in Bayesian optimisation. We show theoretically that the proposed technique is guaranteed to select the most related source and thus helps to improve the optimisation efficiency. We demonstrate the effectiveness of our method for several tasks: synthetic function optimisation, the hyperparameter tuning of support vector machines, and optimisation of short polymer fiber synthesis in an industrial environment.
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Acknowledgment
This research was partially funded by the Australian Government through the Australian Research Council (ARC) and the Telstra-Deakin Centre of Excellence in Big Data and Machine Learning. Professor Venkatesh is the recipient of an ARC Australian Laureate Fellowship (FL170100006). The authors thank Dr Alessandra Sutti and her team for providing short polymer fiber data and several useful discussions.
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Ramachandran, A., Gupta, S., Rana, S., Venkatesh, S. (2018). Selecting Optimal Source for Transfer Learning in Bayesian Optimisation. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_4
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