Stochastic Sampling

Stochastic sampling is a crucial technique for efficiently exploring complex probability distributions, particularly within machine learning and scientific computing. Current research focuses on improving sampling efficiency and effectiveness across diverse applications, including generative modeling (e.g., using diffusion models and neural radiance fields), Bayesian inference (e.g., via Hamilton-Jacobi PDEs), and reinforcement learning (e.g., for combinatorial optimization and traffic control). These advancements are driving progress in areas like image generation, inverse problem solving, and the development of more robust and efficient algorithms for various computational tasks.