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Accelerated Training for Matrix-norm Regularization: A Boosting Approach
Xinhua Zhang · Yao-Liang Yu · Dale Schuurmans

Tue Dec 04 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor
Sparse learning models typically combine a smooth loss with a nonsmooth penalty, such as trace norm. Although recent developments in sparse approximation have offered promising solution methods, current approaches either apply only to matrix-norm constrained problems or provide suboptimal convergence rates. In this paper, we propose a boosting method for regularized learning that guarantees $\epsilon$ accuracy within $O(1/\epsilon)$ iterations. Performance is further accelerated by interlacing boosting with fixed-rank local optimization---exploiting a simpler local objective than previous work. The proposed method yields state-of-the-art performance on large-scale problems. We also demonstrate an application to latent multiview learning for which we provide the first efficient weak-oracle.

Author Information

Xinhua Zhang (University of Illinois at Chicago (UIC))
Yao-Liang Yu (University of Waterloo)
Dale Schuurmans (Google Brain & University of Alberta)

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