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Taking advantage of sparsity in multi-task learning

Lounici, K; Pontil, M; Tsybakov, AB; Van De Geer, SA; (2009) Taking advantage of sparsity in multi-task learning. Presented at: UNSPECIFIED. Green open access


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We study the problem of estimating multiple linear regression equations for the purpose of both prediction and variable selection. Following recent work on multi-task learning [1], we assume that the sparsity patterns of the regression vectors are included in the same set of small cardinality. This assumption leads us to consider the Group Lasso as a candidate estimation method. We show that this estimator enjoys nice sparsity oracle inequalities and variable selection properties. The results hold under a certain restricted eigenvalue condition and a coherence condition on the design matrix, which naturally extend recent work in [3, 19]. In particular, in the multi-task learning scenario, in which the number of tasks can grow, we are able to remove completely the effect of the number of predictor variables in the bounds. Finally, we show how our results can be extended to more general noise distributions, of which we only require the variance to be finite.

Type: Conference item (UNSPECIFIED)
Title: Taking advantage of sparsity in multi-task learning
Open access status: An open access version is available from UCL Discovery
UCL classification: UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/163467
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