Reverse Sampler

Reverse samplers are algorithms used to generate samples from complex probability distributions, particularly those arising in Bayesian inference and generative modeling. Current research focuses on improving the efficiency and stability of these samplers, exploring methods like Hamiltonian Monte Carlo, diffusion models, and variations on score-based approaches, often incorporating techniques from optimal control and regularized latent optimization. These advancements are impacting diverse fields, enabling more accurate and efficient solutions to inverse problems in areas such as image processing, speech enhancement, and Bayesian imaging, ultimately leading to improved model performance and uncertainty quantification.