Conditional Sampling

Conditional sampling aims to generate data points from a probability distribution conditioned on specific input values, a crucial task in various fields. Current research focuses on improving the efficiency and accuracy of conditional sampling within generative models like diffusion models and variational autoencoders, employing techniques such as flow matching, Schrödinger bridges, and classifier-free guidance. These advancements are driving progress in diverse applications, including Bayesian inference, medical image generation, causal inference, and covariate shift adaptation, by enabling more accurate and efficient generation of synthetic data and probabilistic predictions. The development of robust and scalable conditional sampling methods is thus a significant area of ongoing investigation.