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We propose a semi-stochastic Frank-Wolfe algorithm with away-steps forregularized empirical risk minimization and extend it to problems withblock-coordinate structure. Our algorithms use adaptive step-size and we showthat they converge linearly in expectation. The proposed algorithms can beapplied to many important problems in statistics and machine learning includingregularized generalized linear models, support vector machines and many others.In preliminary numerical tests on structural SVM and graph-guided fused LASSO,our algorithms outperform other competing algorithms in both iteration cost andtotal number of data passes.

Semi-Stochastic Frank-Wolfe Algorithms with Away-Steps for Block-Coordinate Structure Problems