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Found 4 papers, 1 papers with code
Information Geometry for the Working Information Theorist
Information geometry is a study of statistical manifolds, that is, spaces of probability distributions from a geometric perspective.
Functional portfolio optimization in stochastic portfolio theory
In this paper we develop a concrete and fully implementable approach to the optimization of functionally generated portfolios in stochastic portfolio theory.
Scalable Gradients for Stochastic Differential Equations
The adjoint sensitivity method scalably computes gradients of solutions to ordinary differential equations.
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Scalable Gradients and Variational Inference for Stochastic Differential Equations
We derive reverse-mode (or adjoint) automatic differentiation for solutions of stochastic differential equations (SDEs), allowing time-efficient and constant-memory computation of pathwise gradients, a continuous-time analogue of the reparameterization trick.
