A Stochastic Interpretation of Stochastic Mirror Descent: Risk-Sensitive Optimality

3 Apr 2019Navid AzizanBabak Hassibi

Stochastic mirror descent (SMD) is a fairly new family of algorithms that has recently found a wide range of applications in optimization, machine learning, and control. It can be considered a generalization of the classical stochastic gradient algorithm (SGD), where instead of updating the weight vector along the negative direction of the stochastic gradient, the update is performed in a "mirror domain" defined by the gradient of a (strictly convex) potential function... (read more)

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