Paper accepted at Transactions on Machine Learning Research


Our paper „An optimal control perspective on diffusion-based generative modeling” has been accepted at the journal Transactions on Machine Learning Research (TMLR), one of the leading journals on machine learning research.

The paper establishes a connection between stochastic optimal control and diffusion based generative models. This perspective allows to transfer methods from optimal control theory to generative modeling and e.g. allows to derive evidence lower bound as a direct consequence of the well-known verification theorem from control theory.

Further, a novel diffusion-based method for sampling from unnormalized densities is developed - a problem frequently occurring in statistics and computational sciences.