2010 MultiTaskLearningforBoostingwit

• (Chapelle et al., 2010) ⇒ Olivier Chapelle, Pannagadatta Shivaswamy, Srinivas Vadrevu, Kilian Weinberger, Ya Zhang, and Belle Tseng. (2010). “Multi-task Learning for Boosting with Application to Web Search Ranking.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835953

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Cited By

• http://scholar.google.com/scholar?q=%22Multi-task+learning+for+boosting+with+application+to+web+search+ranking%22+2010
• http://portal.acm.org/citation.cfm?id=1835953&preflayout=flat#citedby

Quotes

Abstract

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 the specifics of each learning task with task-specific parameters and the commonalities between them through shared parameters. This enables implicit data sharing and regularization. We evaluate our learning method on web-search ranking data sets from several countries. Here, multitask learning is particularly helpful as data sets from different countries vary largely in size because of the cost of editorial judgments. experiments validate that learning various tasks jointly can lead to significant improvements in performance with surprising reliability.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 MultiTaskLearningforBoostingwit	Olivier Chapelle Pannagadatta Shivaswamy Srinivas Vadrevu Kilian Weinberger Ya Zhang Belle Tseng			Multi-task Learning for Boosting with Application to Web Search Ranking		KDD-2010 Proceedings		10.1145/1835804.1835953		2010