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Sampling using Diffusion Processes

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If you have a question about this talk, please contact Prof. Ramji Venkataramanan .

I will discuss a class of diffusion-based algorithms to draw samples from high-dimensional probability distributions given their unnormalized densities. Ideally, the method can transport samples from a Gaussian distribution to a specified target distribution in finite time. The stochastic interpolants framework used to derive a diffusion process, and also involves solving certain Hamilton-Jacobi-Bellman PDEs. These are solved using the theory of forward-backward stochastic differential equations (FBSDE) together with machine learning-based methods. Numerical experiments illustrating that the algorithm will also be discussed. This is joint work with Anand Jerry George.

This talk is part of the Information Theory Seminar series.

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