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Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization
Michael Metel · Akiko Takeda

Wed Jun 12 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #104

Our work focuses on stochastic gradient methods for optimizing a smooth non-convex loss function with a non-smooth non-convex regularizer. Research on this class of problem is quite limited, and until recently no non-asymptotic convergence results have been reported. We present two simple stochastic gradient algorithms, for finite-sum and general stochastic optimization problems, which have superior convergence complexities compared to the current state-of-the-art. We also compare our algorithms' performance in practice for empirical risk minimization.

Author Information

Michael Metel (RIKEN Center for Advanced Intelligence Project)
Akiko Takeda (The University of Tokyo / RIKEN)

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