Boosted multi-task learning

doi:10.1007/s10994-010-5231-6

Boosted multi-task learning

2010-12-24

48

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AI

O

Olivier Chapelle *

P

Pannagadatta K. Shivaswamy

DOI：10.1007/s10994-010-5231-6

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In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing their commonalities through shared parameters and their differences with task-specific ones. This enables implicit data sharing and regularization. Our algorithm is derived using the relationship between a (1)-regularization and boosting. We evaluate our learning method on web-search ranking data sets from several countries. Here, multi-task learning is particularly helpful as data sets from different countries vary largely in size because of the cost of editorial judgments. Further, the proposed method obtains state-of-the-art results on a publicly available multi-task dataset. Our experiments validate that learning various tasks jointly can lead to significant improvements in performance with surprising reliability.

Keywords:

Multi-task learning

Boosting

Decision trees

Web search

Ranking

Boosted multi-task learning

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