gives the solution to . Special cases Spectral penalties - Dinnuzo et al suggested setting F as the Frobenius norm . They optimized directly using block coordinate descent, not accounting for difficulties at the boundary of . Clustered tasks learning - Jacob et al suggested to learn A in the setting where T tasks are organized in R disjoint clusters. In this case let be the matrix with . Setting , and , the task matrix can be parameterized as a function of : , with terms that penalize the average, between clusters variance and within clusters variance respectively of the task predictions. M is not convex, but there is a convex relaxation . In this formulation, . Generalizations Non-convex penalties - Penalties can be constructed such that A is constrained to be a graph Laplacian, or that A has low rank factorization. However these penalties are not convex, and the analysis of the barrier method proposed by Ciliberto et al. does not go through in these cases. Non-separable kernels - Separable kernels are limited, in particular they do not account for structures in the interaction space between the input and output domains jointly. Future work is needed to develop models for these kernels. Applications Spam filtering Using the principles of MTL, techniques for collaborative spam filtering that facilitates personalization have been proposed. In large scale open membership email systems, most users do not label enough messages for an individual local classifier to be effective, while the data is too noisy to be used for a global filter across all users. A hybrid global/individual classifier can be effective at absorbing the influence of users who label emails very diligently from the general public. This can be accomplished while still providing sufficient quality to users with few labeled instances. Web search Using boosted decision trees, one can enable implicit data sharing and regularization. This learning method can be used on web-search ranking data sets. One example is to use 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. It has been demonstrated that learning various tasks jointly can lead to significant improvements in performance with surprising reliability. Software package The Multi-Task Learning via StructurAl Regularization (MALSAR) Matlab package implements the following multi-task learning algorithms: Mean-Regularized Multi-Task Learning Multi-Task Learning with Joint