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Stochastic Differential Equations

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Stochastic Differential Equations (SDEs) have a range of applications from physics to finance and have attracted attention in the machine learning community. We will give a brief overview of some of the theoretical results concerning SDEs, including the formulation of SDEs via the Ito integral, the Fokker-Planck Equation and simulation via the Euler-Maruyama method. We will also highlight important machine learning applications of SDEs in optimization (Stochastic Gradient Langevin Dynamics) and the strong connections between SDEs and Gaussian processes.

This talk is part of the Machine Learning Reading Group @ CUED series.

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