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Key to multitask learning is exploiting relationships between different tasksto improve prediction performance. If the relations are linear, regularizationapproaches can be used successfully. However, in practice assuming the tasks tobe linearly related might be restrictive, and allowing for nonlinear structuresis a challenge. In this paper, we tackle this issue by casting the problemwithin the framework of structured prediction. Our main contribution is a novelalgorithm for learning multiple tasks which are related by a system ofnonlinear equations that their joint outputs need to satisfy. We show that thealgorithm is consistent and can be efficiently implemented. Experimentalresults show the potential of the proposed method.

Consistent Multitask Learning with Nonlinear Output Relations