Accelerated Sampling

Accelerated sampling aims to drastically reduce the computational cost of generating samples from complex probability distributions, a crucial task across diverse scientific fields. Current research focuses on improving the efficiency of existing algorithms like Hamiltonian Monte Carlo and diffusion models, often employing techniques such as early exiting, gradient-accelerated sampling, and importance sampling driven by neural networks. These advancements are significantly impacting fields ranging from robotics and materials science to Bayesian inference and deep learning, enabling more efficient exploration of high-dimensional spaces and faster analysis of large datasets.