Research Group Vincent Fortuin
Vincent Fortuin
Dr.
Associate
Vincent Fortuin
His research focuses on reliable and data-efficient AI approaches leveraging Bayesian deep learning, deep generative modeling, meta-learning, and PAC-Bayesian theory.
Team members @MCML
PostDocs
James Odgers
Dr.
→ Group Vincent Fortuin
Bayesian Deep Learning
PhD Students
Blagovest Gospodinov
→ Group Vincent Fortuin
Bayesian Deep Learning
Ben Riegler
→ Group Vincent Fortuin
Bayesian Deep Learning
Thomas Rochussen
→ Group Vincent Fortuin
Bayesian Deep Learning
Annika Schneider
→ Group Vincent Fortuin
Bayesian Deep Learning
Arsen Sheverdin
→ Group Vincent Fortuin
Bayesian Deep Learning
Recent News @MCML
Publications @MCML
2025
[11]
M. Kmicikiewicz • V. Fortuin • E. Szczurek
ProSpero: Active Learning for Robust Protein Design Beyond Wild-Type Neighborhoods.
NeurIPS 2025 - 39th Conference on Neural Information Processing Systems. San Diego, CA, USA, Nov 30-Dec 07, 2025. To be published. Preprint available. URL
ProSpero: Active Learning for Robust Protein Design Beyond Wild-Type Neighborhoods.
NeurIPS 2025 - 39th Conference on Neural Information Processing Systems. San Diego, CA, USA, Nov 30-Dec 07, 2025. To be published. Preprint available. URL
[10]
A. Reuter • T. G. J. Rudner • V. Fortuin • D. Rügamer
Can Transformers Learn Full Bayesian Inference in Context?
ICML 2025 - 42nd International Conference on Machine Learning. Vancouver, Canada, Jul 13-19, 2025. URL
Can Transformers Learn Full Bayesian Inference in Context?
ICML 2025 - 42nd International Conference on Machine Learning. Vancouver, Canada, Jul 13-19, 2025. URL
[9]
T. Rochussen • V. Fortuin
Sparse Gaussian Neural Processes.
AABI 2025 - 7th Symposium on Advances in Approximate Bayesian Inference collocated with the 13th International Conference on Learning Representations. Singapore, Apr 29, 2025. To be published. Preprint available. arXiv
Sparse Gaussian Neural Processes.
AABI 2025 - 7th Symposium on Advances in Approximate Bayesian Inference collocated with the 13th International Conference on Learning Representations. Singapore, Apr 29, 2025. To be published. Preprint available. arXiv
[8]
A. Reuter • T. G. J. Rudner • V. Fortuin • D. Rügamer
Can Transformers Learn Full Bayesian Inference in Context?
FPI @ICLR 2025 - Workshop on Frontiers in Probabilistic Inference: Learning meets Sampling at the 13th International Conference on Learning Representations. Singapore, Apr 24-28, 2025. arXiv URL
Can Transformers Learn Full Bayesian Inference in Context?
FPI @ICLR 2025 - Workshop on Frontiers in Probabilistic Inference: Learning meets Sampling at the 13th International Conference on Learning Representations. Singapore, Apr 24-28, 2025. arXiv URL
[7]
A. Reuter • T. G. J. Rudner • V. Fortuin • D. Rügamer
Can Transformers Learn Full Bayesian Inference in Context?
SynthData @ICLR 2025 - Workshop SynthData: Will Synthetic Data Finally Solve the Data Access Problem? at the 13th International Conference on Learning Representations. Singapore, Apr 24-28, 2025. URL
Can Transformers Learn Full Bayesian Inference in Context?
SynthData @ICLR 2025 - Workshop SynthData: Will Synthetic Data Finally Solve the Data Access Problem? at the 13th International Conference on Learning Representations. Singapore, Apr 24-28, 2025. URL
2024
[6]
R. Dhahri • A. Immer • B. Charpentier • S. Günnemann • V. Fortuin
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood.
NeurIPS 2024 - 38th Conference on Neural Information Processing Systems. Vancouver, Canada, Dec 10-15, 2024. URL
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood.
NeurIPS 2024 - 38th Conference on Neural Information Processing Systems. Vancouver, Canada, Dec 10-15, 2024. URL
[5]
K. Flöge • M. A. Moeed • V. Fortuin
Stein Variational Newton Neural Network Ensembles.
Preprint (Nov. 2024). arXiv
Stein Variational Newton Neural Network Ensembles.
Preprint (Nov. 2024). arXiv
[4]
K. Flöge • S. Udayakumar • J. Sommer • M. Piraud • S. Kesselheim • V. Fortuin • S. Günneman • K. J. van der Weg • H. Gohlke • E. Merdivan • A. Bazarova
OneProt: Towards Multi-Modal Protein Foundation Models.
Preprint (Nov. 2024). arXiv
OneProt: Towards Multi-Modal Protein Foundation Models.
Preprint (Nov. 2024). arXiv
[3]
K. Bouchiat • A. Immer • H. Yèche • G. Ratsch • V. Fortuin
Improving Neural Additive Models with Bayesian Principles.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Improving Neural Additive Models with Bayesian Principles.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
[2]
T. Papamarkou • M. Skoularidou • K. Palla • L. Aitchison • J. Arbel • D. Dunson • M. Filippone • V. Fortuin • P. Hennig • J. M. Hernández-Lobato • A. Hubin • A. Immer • T. Karaletsos • M. E. Khan • A. Kristiadi • Y. Li • S. Mandt • C. Nemeth • M. A. Osborne • T. G. J. Rudner • D. Rügamer • Y. W. Teh • M. Welling • A. G. Wilson • R. Zhang
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI.
ICML 2024 - 41st International Conference on Machine Learning. Vienna, Austria, Jul 21-27, 2024. URL
[1]
F. Sergeev • P. Malsot • G. Rätsch • V. Fortuin
Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information.
Preprint (Jul. 2024). arXiv
Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information.
Preprint (Jul. 2024). arXiv
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