Online Boosting Algorithms for Anytime Transfer and Multitask Learning


  • Boyu Wang McGill University
  • Joelle Pineau McGill University



The related problems of transfer learning and multitask learning have attracted significant attention, generating a rich literature of models and algorithms. Yet most existing approaches are studied in an offline fashion, implicitly assuming that data from different domains are given as a batch. Such an assumption is not valid in many real-world applications where data samples arrive sequentially, and one wants a good learner even from few examples. The goal of our work is to provide sound extensions to existing transfer and multitask learning algorithms such that they can be used in an anytime setting. More specifically, we propose two novel online boosting algorithms, one for transfer learning and one for multitask learning, both designed to leverage the knowledge of instances in other domains. The experimental results show state-of-the-art empirical performance on standard benchmarks, and we present results of using our methods for effectively detecting new seizures in patients with epilepsy from very few previous samples.




How to Cite

Wang, B., & Pineau, J. (2015). Online Boosting Algorithms for Anytime Transfer and Multitask Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1).



Main Track: Novel Machine Learning Algorithms