Fast Sampling

Fast sampling aims to efficiently generate representative samples from complex probability distributions, crucial for various scientific and engineering applications where exact sampling is intractable. Current research focuses on improving the speed and quality of sampling using diverse approaches, including diffusion models (often accelerated via novel ODE and SDE solvers), Markov chain Monte Carlo methods (enhanced with techniques like adaptive teachers and involutive maps), and other techniques like predictor-corrector methods and stochastic gradient proximal samplers. These advancements are significantly impacting fields like molecular dynamics, image generation, and Bayesian inference by enabling faster model training, more efficient exploration of high-dimensional spaces, and improved accuracy in complex simulations.