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Poster
A Stochastic Gradient Method with an Exponential Convergence 
Rate for Finite Training Sets
Nicolas Le Roux · Mark Schmidt · Francis Bach

Tue Dec 04 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor
in Poster Session »

We propose a new stochastic gradient method for optimizing the sum of
 a finite set of smooth functions, where the sum is strongly convex.
 While standard stochastic gradient methods
 converge at sublinear rates for this problem, the proposed method incorporates a memory of previous gradient values in order to achieve a linear convergence 
rate. In a machine learning context, numerical experiments indicate that the new algorithm can dramatically outperform standard
 algorithms, both in terms of optimizing the training error and reducing the test error quickly.

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Author Information

Nicolas Le Roux (Microsoft Research)
Mark Schmidt (INRIA - SIERRA Project Team)
Francis Bach (INRIA - Ecole Normale Superieure)

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