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Scalable stochastic gradient descent with improved confidence

Stochastic gradient descent methods have been quite successful for solving largescale and online learning problems. We provide a simple parallel framework to obtain solutions of high confidence, where the confidence can be easily controlled by the number of processes, independently of the length of learning processes. Our framework is implemented as a scalable open-source software which can be configured for a single multicore machine or for a cluster of computers, where the training outcomes from independent parallel processes are combined to produce the final output.