Approximate Manifold Sampling

AI generated image representing samples around a manifold.


Sampling from a probability density concentrated around a lower-dimensional submanifold is of importance in numerous applications arising in machine learning, statistics and statistical physics. This task is particularly challenging due to the extreme anisotropy and high-dimensionality of the problem, and the correlation between the variables. We propose a new family of bespoke algorithms to sample efficiently from these densities and show their computational superiority to general purpose and specialized samplers. In particular, we show that in some cases it is able to efficiently sample from densities that are several orders of magnitude tighter around the submanifold.

Mar 15, 2023 9:00 AM — Mar 17, 2023 5:00 PM
Levi, Finland
Mauro Camara Escudero
Computational Statistics and Data Science Ph.D.

My research interests include approximate manifold sampling and generative models.