Conditional Distribution

Conditional distribution learning aims to model the probability of an outcome given specific input conditions, a crucial task across numerous fields. Current research emphasizes developing robust and efficient methods for estimating these distributions, particularly focusing on deep generative models (like diffusion models and normalizing flows), optimal transport techniques, and Bayesian approaches. These advancements are driving improvements in diverse applications, including data assimilation, semi-supervised learning, and uncertainty quantification in machine learning, by enabling more accurate predictions and better understanding of complex relationships within data.