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math.OC2023
A Constructive Approach to Function Realization by Neural Stochastic Differential Equations
Tanya Veeravalli, Maxim Raginsky
The problem of function approximation by neural dynamical systems has typically been approached in a top-down manner: Any continuous function can be approximated to an arbitrary ac…
math.OC2023
Variational Principles for Mirror Descent and Mirror Langevin Dynamics
Belinda Tzen, Anant Raj, Maxim Raginsky +1
Mirror descent, introduced by Nemirovski and Yudin in the 1970s, is a primal-dual convex optimization method that can be tailored to the geometry of the optimization problem at han…
math.OC2022
Nonlinear controllability and function representation by neural stochastic differential equations
Tanya Veeravalli, Maxim Raginsky
There has been a great deal of recent interest in learning and approximation of functions that can be expressed as expectations of a given nonlinearity with respect to its random i…