Machine Learning - Special issue on inductive transfer
IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Boosting as a Regularized Path to a Maximum Margin Classifier
The Journal of Machine Learning Research
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
A unified architecture for natural language processing: deep neural networks with multitask learning
Proceedings of the 25th international conference on Machine learning
Feature hashing for large scale multitask learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Model adaptation via model interpolation and boosting for web search ranking
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Knowledge transfer for cross domain learning to rank
Information Retrieval
Relevant knowledge helps in choosing right teacher: active query selection for ranking adaptation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Flexible sample selection strategies for transfer learning in ranking
Information Processing and Management: an International Journal
Classifier ensemble recommendation
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Learning multiple tasks with boosted decision trees
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Multi-Task boosting by exploiting task relationships
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Sentiment and topic analysis on social media: a multi-task multi-label classification approach
Proceedings of the 5th Annual ACM Web Science Conference
Collaborative boosting for activity classification in microblogs
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning high-order task relationships in multi-task learning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
User demographics prediction based on mobile data
Pervasive and Mobile Computing
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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 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. Our experiments validate that learning various tasks jointly can lead to significant improvements in performance with surprising reliability.