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Flexible transfer learning framework for bayesian optimisation

chapter
posted on 2016-04-12, 00:00 authored by T Joy, Santu RanaSantu Rana, Sunil GuptaSunil Gupta, Svetha VenkateshSvetha Venkatesh
Bayesian optimisation is an efficient technique to optimise functions that are expensive to compute. In this paper, we propose a novel framework to transfer knowledge from a completed source optimisation task to a new target task in order to overcome the cold start problem. We model source data as noisy observations of the target function. The level of noise is computed from the data in a Bayesian setting. This enables flexible knowledge transfer across tasks with differing relatedness, addressing a limitation of the existing methods. We evaluate on the task of tuning hyperparameters of two machine learning algorithms. Treating a fraction of the whole training data as source and the whole as the target task, we show that our method finds the best hyperparameters in the least amount of time compared to both the state-of-art and no transfer method.

History

Volume

9651

Chapter number

9

Pagination

102-114

ISSN

0302-9743

ISBN-13

9783319317533

Language

eng

Publication classification

B Book chapter, B1 Book chapter

Copyright notice

2016, Springer

Extent

47

Editor/Contributor(s)

Bailey J, Khan L, Washio T, Dobbie G, Huang JZ, Wang R

Publisher

Springer

Place of publication

Berlin, Germany

Title of book

Advances in knowledge discovery and data mining: 20th Pacific-Asia Conference, PAKDD 2016 Auckland, New Zealand, April 19-22, 2016 proceedings, part I

Series

Lecture notes in artificial intelligence; v.9651