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We present novel minibatch stochastic optimization methods for empirical riskminimization problems, the methods efficiently leverage variance reducedfirst-order and sub-sampled higher-order information to accelerate theconvergence speed. For quadratic objectives, we prove improved iterationcomplexity over state-of-the-art under reasonable assumptions. We also provideempirical evidence of the advantages of our method compared to existingapproaches in the literature.

Improved Optimization of Finite Sums with Minibatch Stochastic Variance Reduced Proximal Iterations