Theoretical Analysis of Continuous Normalizing Flows for Learning Probability Distributions

Continuous normalizing flows (CNFs) are a generative method for learning probability distributions from finite random samples. This work establishes non-asymptotic error bounds for the distribution estimator based on CNFs with linear interpolation and flow matching, under assumptions on the target distribution.