A PAC-Bayesian bound for Lifelong Learning Conference Paper


Author(s): Pentina, Anastasia; Lampert, Christoph
Title: A PAC-Bayesian bound for Lifelong Learning
Affiliation IST Austria
Abstract: Transfer learning has received a lot of attention in the machine learning community over the last years, and several effective algorithms have been developed. However, relatively little is known about their theoretical properties, especially in the setting of lifelong learning, where the goal is to transfer information to tasks for which no data have been observed so far. In this work we study lifelong learning from a theoretical perspective. Our main result is a PAC-Bayesian generalization bound that offers a unified view on existing paradigms for transfer learning, such as the transfer of parameters or the transfer of low-dimensional representations. We also use the bound to derive two principled lifelong learning algorithms, and we show that these yield results comparable with existing methods.
Conference Title: ICML: International Conference on Machine Learning
Volume: 32
Conference Dates: June 21 - June 26, 2014
Conference Location: Beijing, China
Publisher: Omnipress  
Date Published: 2014-05-10
Start Page: 991
End Page: 999
URL:
Open access: yes (repository)
IST Austria Authors
  1. Christoph Lampert
    87 Lampert
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