A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization

13 Apr 2016 • Shun Zheng • Jialei Wang • Fen Xia • Wei Xu • Tong Zhang

In modern large-scale machine learning applications, the training data are often partitioned and stored on multiple machines. It is customary to employ the "data parallelism" approach, where the aggregated training loss is minimized without moving data across machines... (read more)

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