Encoding tree sparsity in multi-task learning: A probabilistic framework

Lei Han, Yu ZHANG, Guojie Song*, Kunqing Xie

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

15 Citations (Scopus)

Abstract

Multi-task learning seeks to improve the generalization performance by sharing common information among multiple related tasks. A key assumption in most MTL algorithms is that all tasks are related, which, however, may not hold in many real-world applications. Existing techniques, which attempt to address this issue, aim to identify groups of related tasks using group sparsity. In this paper, we propose a probabilistic tree sparsity (PTS) model to utilize the tree structure to obtain the sparse solution instead of the group structure. Specifically, each model coefficient in the learning model is decomposed into a product of multiple component coefficients each of which corresponds to a node in the tree. Based on the decomposition, Gaussian and Cauchy distributions are placed on the component coefficients as priors to restrict the model complexity. We devise an efficient expectation maximization algorithm to learn the model parameters. Experiments conducted on both synthetic and real-world problems show the effectiveness of our model compared with state-of-the-art baselines.

Original languageEnglish
Title of host publicationProceedings of the 28th AAAI Conference on Artificial Intelligence and the 26th Innovative Applications of Artificial Intelligence Conference and the 5th Symposium on Educational Advances in Artificial Intelligence
PublisherAAAI press
Pages1854-1860
Number of pages7
ISBN (Electronic)9781577356790
ISBN (Print)9781577356615, 9781577356776
Publication statusPublished - 2014
Event28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014 - Quebec, Canada, Quebec City, Canada
Duration: 27 Jul 201431 Jul 2014

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume3

Conference

Conference28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014
Country/TerritoryCanada
CityQuebec City
Period27/07/1431/07/14

Scopus Subject Areas

  • Software
  • Artificial Intelligence

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