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We develop a cyclical blockwise coordinate descent algorithm for the multi-task Lasso that efficiently solves problems with thousands of features and tasks. The main result shows that a closed-form Winsorization operator can be obtained for the sup-norm penalized least squares regression. This allows the algorithm to find solutions to very largescale problems far more efficiently than existing methods. This result complements the pioneering work of Friedman, et al. (2007) for the single-taskdoi:10.1145/1553374.1553458 dblp:conf/icml/LiuPZ09 fatcat:kfuwlmdwynhrtinyhr7hcd4f7a