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A Full Adagrad algorithm with O(Nd) operations
A novel approach is given to overcome the computational challenges of the full-matrix Adaptive Gradient algorithm (Full AdaGrad) in stochastic optimization.
Online estimation of the inverse of the Hessian for stochastic optimization with application to universal stochastic Newton algorithms
This paper addresses second-order stochastic optimization for estimating the minimizer of a convex function written as an expectation.
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