Christopher von Klitzing — accepted AI-conference papers

2026

Efficient Training of Boltzmann Generators Using Off-Policy Log-Dispersion Regularization

Henrik Schopmans, Christopher von Klitzing, Pascal Friederich

ICML 2026poster

Sampling from unnormalized probability densities is a central challenge in computational science. Boltzmann generators are generative models that enable independent sampling from the Boltzmann distribution of physical systems at a given temperature. However, their practical success depends on data-e…

Cited by 1 Source ↗Scholar ↗

2026

Learning Boltzmann Generators via Constrained Mass Transport

Christopher von Klitzing, Denis Blessing, Henrik Schopmans, Pascal Friederich, Gerhard Neumann

ICLR 2026poster

Efficient sampling from high-dimensional and multimodal unnormalized probability distributions is a central challenge in many areas of science and machine learning. We focus on Boltzmann generators (BGs) that aim to sample the Boltzmann distribution of physical systems, such as molecules, at a given…

Cited by 0 Source ↗Scholar ↗