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Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning
Published on Jan 19, 20123863 Views
We consider the minimization of a convex objective function defined on a Hilbert space, which is only available through unbiased estimates of its gradients. This problem includes standard machine lear
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Chapter list
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning00:00
Stochastic approximation00:20
Convex stochastic approximation01:05
Summary of new results02:00