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stat.ML2024
Importance Corrected Neural JKO Sampling
Johannes Hertrich, Robert Gruhlke
In order to sample from an unnormalized probability density function, we propose to combine continuous normalizing flows (CNFs) with rejection-resampling steps based on importance…
stat.ML2024
Generative Modelling with Tensor Train approximations of Hamilton--Jacobi--Bellman equations
David Sommer, Robert Gruhlke, Max Kirstein +2
Sampling from probability densities is a common challenge in fields such as Uncertainty Quantification (UQ) and Generative Modelling (GM). In GM in particular, the use of reverse-t…
math.OC2024
Optimal sampling for stochastic and natural gradient descent
Robert Gruhlke, Anthony Nouy, Philipp Trunschke
We consider the problem of optimising the expected value of a loss functional over a nonlinear model class of functions, assuming that we have only access to realisations of the gr…