ProbNum: Probabilistic Numerics in Python. - Jonathan Wenger; Nicholas Krämer; Marvin Pförtner; Jonathan Schmidt 001; Nathanael Bosch; Nina Effenberger; Johannes Zenn; Alexandra Gessner; Toni Karvonen; François-Xavier Briol; Maren Mahsereci; Philipp Hennig (CoRR, 2021)

Resampling Gradients Vanish in Differentiable Sequential Monte Carlo Samplers. - Johannes Zenn; Robert Bamler (Tiny Papers @ ICLR, 2023)

Upgrading VAE Training With Unlimited Data Plans Provided by Diffusion Models. - Tim Z. Xiao; Johannes Zenn; Robert Bamler (CoRR, 2023)

The SVHN Dataset Is Deceptive for Probabilistic Generative Models Due to a Distribution Mismatch. - Tim Z. Xiao; Johannes Zenn; Robert Bamler (CoRR, 2023)

Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence Between Initial and Target Distribution. - Johannes Zenn; Robert Bamler (ICML, 2024)

Balancing Molecular Information and Empirical Data in the Prediction of Physico-Chemical Properties. - Johannes Zenn; Dominik Gond; Fabian Jirasek; Robert Bamler (CoRR, 2024)

A Note on Generalization in Variational Autoencoders: How Effective Is Synthetic Data and Overparameterization? - Tim Z. Xiao; Johannes Zenn; Robert Bamler (Trans. Mach. Learn. Res., 2025)

Flipping Against All Odds: Reducing LLM Coin Flip Bias via Verbalized Rejection Sampling. - Tim Z. Xiao; Johannes Zenn; Zhen Liu 019; Weiyang Liu; Robert Bamler; Bernhard Schölkopf (CoRR, 2025)