Stein Diffusion Guidance corrects approximate posteriors in diffusion sampling via a Stein variational mechanism and surrogate SOC objective to enable effective guidance beyond high-density regimes.
A simulation-free deep learning approach to stochastic optimal control
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A derivative-free projection algorithm with randomized neural networks solves high-dimensional stochastic optimal control and mean field control problems by regressing controls instead of minimizing loss via backpropagation.
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Stein Diffusion Guidance: Training-Free Posterior Correction for Sampling Beyond High-Density Regions
Stein Diffusion Guidance corrects approximate posteriors in diffusion sampling via a Stein variational mechanism and surrogate SOC objective to enable effective guidance beyond high-density regimes.
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An Effective Particle Gradient Projection Method for Solving Stochastic and Mean Field Control Problem
A derivative-free projection algorithm with randomized neural networks solves high-dimensional stochastic optimal control and mean field control problems by regressing controls instead of minimizing loss via backpropagation.