Publication Highlights
For a complete list of publications, please refer to my Google Scholar profile.
ICML 2026 Can Microcanonical Langevin Dynamics Leverage Mini-Batch Gradient Noise?
Emanuel Sommer*, Kangning Diao*, Jakob Robnik, Uros Seljak, David Rügamer
Bayesian Deep Learning Microcanonical Langevin Scalability
ICML 2026 (Spotlight) Position: The Time for Sampling Is Now! Charting a New Course for Bayesian Deep Learning
Emanuel Sommer, David Rügamer
Bayesian Deep Learning UQ Posterior Distillation
AISTATS 2026 (Spotlight) On the Interplay of Priors and Overparametrization in Bayesian Neural Network Posteriors
Julius Kobialka*, Emanuel Sommer*, Chris Kolb, Juntae Kwon, Daniel Dold, David Rügamer
Bayesian Deep Learning Sampling-based Inference BNN Priors Overparameterization
ICLR 2025 Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks
Emanuel Sommer, Jakob Robnik, Giorgi Nozadze, Uros Seljak, David Rügamer
Bayesian Deep Learning Sampling-based Inference MCMC UQ
ICML 2024 Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
Emanuel Sommer*, Lisa Wimmer*, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl, David Rügamer
BNNs Mode-Connectedness Sampling-based Inference Symmetries
* Equal contribution
