Rejection Sampling

Rejection sampling is a technique used to generate samples from a complex probability distribution by drawing samples from a simpler proposal distribution and accepting or rejecting them based on a probability ratio. Current research focuses on improving the efficiency and effectiveness of rejection sampling within various machine learning models, including diffusion models, generative adversarial networks (GANs), and large language models, often by integrating it with other techniques like importance sampling or neural networks to address challenges like mode collapse and data privacy. These advancements are significantly impacting fields like image synthesis, natural language processing, and causal inference by enabling the generation of high-quality samples from intricate distributions and improving the robustness of machine learning models. The development of optimal budgeted rejection sampling and reparameterized variants further enhances its practical applicability.