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A Dirty Model for Multi-task Learning

A Dirty Model for Multi-task Learning

This video was recorded at 24th Annual Conference on Neural Information Processing Systems (NIPS), Vancouver 2010. We consider the multiple linear regression problem, in a setting where some of the set of relevant features could be shared across the tasks. A lot of recent research has studied the use of L1 Lq norm block-regularizations with q and 1 for such (possibly) block-structured problems, establishing strong guarantees on recovery even under high-dimensional scaling where the number of features scale with the number of observations. However, these papers also caution that the performance of such block-regularized methods are very dependent on the to which the features are shared across tasks. Indeed they show that if the extent of overlap is less than a threshold, or even if parameter... Show More

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