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Blockwise Coordinate Descent Procedures for the Multi-Task Lasso with Applications to Neural Semantic Basis Discovery

Blockwise Coordinate Descent Procedures for the Multi-Task Lasso with Applications to Neural Semantic Basis Discovery

This video was recorded at 26th International Conference on Machine Learning (ICML), Montreal 2009. 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 large-scale problems far more efficiently than existing methods. This result complements the pioneering work of Friedman, et al. (2007) for the single-task Lasso. As a case study, we use the multi-task Lasso as a variable selector to discover a semantic basis for predicting human neural activation. The learned solution outperforms the standard basis for... Show More
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