InspireMe: Learning Sequence Models for Stories. - Vincent Fortuin; Romann M. Weber; Sasha Schriber; Diana Wotruba; Markus H. Gross (AAAI, 2018)

Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series. - Vincent Fortuin; Matthias Hüser; Francesco Locatello; Heiko Strathmann; Gunnar Rätsch (CoRR, 2018)

Scalable Gaussian Processes on Discrete Domains. - Vincent Fortuin; Gideon Dresdner; Heiko Strathmann; Gunnar Rätsch (CoRR, 2018)

SOM-VAE: Interpretable Discrete Representation Learning on Time Series. - Vincent Fortuin; Matthias Hüser; Francesco Locatello; Heiko Strathmann; Gunnar Rätsch (ICLR (Poster), 2019)

Deep Mean Functions for Meta-Learning in Gaussian Processes. - Vincent Fortuin; Gunnar Rätsch (CoRR, 2019)

Multivariate Time Series Imputation with Variational Autoencoders. - Vincent Fortuin; Gunnar Rätsch; Stephan Mandt (CoRR, 2019)

MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis. - Margherita Rosnati; Vincent Fortuin (CoRR, 2019)

Deep Multiple Instance Learning for Taxonomic Classification of Metagenomic read sets. - Andreas Georgiou; Vincent Fortuin; Harun Mustafa; Gunnar Rätsch (CoRR, 2019)

Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps. - Laura Manduchi; Matthias Hüser; Gunnar Rätsch; Vincent Fortuin (CoRR, 2019)

Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations. - Andreas Kopf; Vincent Fortuin; Vignesh Ram Somnath; Manfred Claassen (CoRR, 2019)

GP-VAE: Deep Probabilistic Time Series Imputation. - Vincent Fortuin; Dmitry Baranchuk; Gunnar Rätsch; Stephan Mandt (AISTATS, 2020)

Conservative Uncertainty Estimation By Fitting Prior Networks. - Kamil Ciosek; Vincent Fortuin; Ryota Tomioka; Katja Hofmann; Richard E. Turner (ICLR, 2020)

PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees. - Jonas Rothfuss; Vincent Fortuin; Andreas Krause 001 (CoRR, 2020)

Sparse Gaussian Process Variational Autoencoders. - Matthew Ashman; Jonathan So; William Tebbutt; Vincent Fortuin; Michael Pearce; Richard E. Turner (CoRR, 2020)

Scalable Gaussian Process Variational Autoencoders. - Metod Jazbec; Vincent Fortuin; Michael Pearce; Stephan Mandt; Gunnar Rätsch (CoRR, 2020)

Factorized Gaussian Process Variational Autoencoders. - Metod Jazbec; Michael Pearce; Vincent Fortuin (CoRR, 2020)

Scalable Gaussian Process Variational Autoencoders. - Metod Jazbec; Matthew Ashman; Vincent Fortuin; Michael Pearce; Stephan Mandt; Gunnar Rätsch (AISTATS, 2021)

T-DPSOM: an interpretable clustering method for unsupervised learning of patient health states. - Laura Manduchi; Matthias Hüser; Martin Faltys; Julia E. Vogt; Gunnar Rätsch; Vincent Fortuin (CHIL, 2021)

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. - Alexander Immer; Matthias Bauer 001; Vincent Fortuin; Gunnar Rätsch; Mohammad Emtiyaz Khan (ICML, 2021)

PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees. - Jonas Rothfuss; Vincent Fortuin; Martin Josifoski; Andreas Krause 001 (ICML, 2021)

Repulsive Deep Ensembles are Bayesian. - Francesco D'Angelo; Vincent Fortuin (NeurIPS, 2021)

Sparse Gaussian Processes on Discrete Domains. - Vincent Fortuin; Gideon Dresdner; Heiko Strathmann; Gunnar Rätsch (IEEE Access, 2021)

Annealed Stein Variational Gradient Descent. - Francesco D'Angelo; Vincent Fortuin (CoRR, 2021)

Exact Langevin Dynamics with Stochastic Gradients. - Adrià Garriga-Alonso; Vincent Fortuin (CoRR, 2021)

On Disentanglement in Gaussian Process Variational Autoencoders. - Simon Bing; Vincent Fortuin; Gunnar Rätsch (CoRR, 2021)

Bayesian Neural Network Priors Revisited. - Vincent Fortuin; Adrià Garriga-Alonso; Florian Wenzel; Gunnar Rätsch; Richard E. Turner; Mark van der Wilk; Laurence Aitchison (CoRR, 2021)

Priors in Bayesian Deep Learning: A Review. - Vincent Fortuin (CoRR, 2021)

BNNpriors: A library for Bayesian neural network inference with different prior distributions. - Vincent Fortuin; Adrià Garriga-Alonso; Mark van der Wilk; Laurence Aitchison (CoRR, 2021)

On Stein Variational Neural Network Ensembles. - Francesco D'Angelo; Vincent Fortuin; Florian Wenzel (CoRR, 2021)

A Bayesian Approach to Invariant Deep Neural Networks. - Nikolaos Mourdoukoutas; Marco Federici; Georges Pantalos; Mark van der Wilk; Vincent Fortuin (CoRR, 2021)

Neural Variational Gradient Descent. - Lauro Langosco di Langosco; Vincent Fortuin; Heiko Strathmann (CoRR, 2021)

Pathologies in priors and inference for Bayesian transformers. - Tristan Cinquin; Alexander Immer; Max Horn; Vincent Fortuin (CoRR, 2021)

Probing as Quantifying the Inductive Bias of Pre-trained Representations. - Alexander Immer; Lucas Torroba Hennigen; Vincent Fortuin; Ryan Cotterell (CoRR, 2021)

Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data. - Andreas Kopf; Vincent Fortuin; Vignesh Ram Somnath; Manfred Claassen (PLoS Comput. Biol., 2021)

BNNpriors: A library for Bayesian neural network inference with different prior distributions. - Vincent Fortuin; Adrià Garriga-Alonso; Mark van der Wilk; Laurence Aitchison (Softw. Impacts, 2021)

On the Choice of Priors in Bayesian Deep Learning. - Vincent Fortuin (2021)

Probing as Quantifying Inductive Bias. - Alexander Immer; Lucas Torroba Hennigen; Vincent Fortuin; Ryan Cotterell (ACL (1), 2022)

Bayesian Neural Network Priors Revisited. - Vincent Fortuin; Adrià Garriga-Alonso; Sebastian W. Ober; Florian Wenzel; Gunnar Rätsch; Richard E. Turner; Mark van der Wilk; Laurence Aitchison (ICLR, 2022)

Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. - Alexander Immer; Tycho F. A. van der Ouderaa; Gunnar Rätsch; Vincent Fortuin; Mark van der Wilk (NeurIPS, 2022)

On Interpretable Reranking-Based Dependency Parsing Systems. - Florian Schottmann; Vincent Fortuin; Edoardo M. Ponti; Ryan Cotterell (SwissText, 2022)

Data augmentation in Bayesian neural networks and the cold posterior effect. - Seth Nabarro; Stoil Ganev; Adrià Garriga-Alonso; Vincent Fortuin; Mark van der Wilk; Laurence Aitchison (UAI, 2022)

PAC-Bayesian Meta-Learning: From Theory to Practice. - Jonas Rothfuss; Martin Josifoski; Vincent Fortuin; Andreas Krause 001 (CoRR, 2022)

Sparse MoEs meet Efficient Ensembles. - James Urquhart Allingham; Florian Wenzel; Zelda E. Mariet; Basil Mustafa; Joan Puigcerver; Neil Houlsby; Ghassen Jerfel; Vincent Fortuin; Balaji Lakshminarayanan; Jasper Snoek; Dustin Tran; Carlos Riquelme Ruiz; et al. (Trans. Mach. Learn. Res., 2022)

Deep Classifiers with Label Noise Modeling and Distance Awareness. - Vincent Fortuin; Mark Collier; Florian Wenzel; James Urquhart Allingham; Jeremiah Zhe Liu; Dustin Tran; Balaji Lakshminarayanan; Jesse Berent; Rodolphe Jenatton; Effrosyni Kokiopoulou (Trans. Mach. Learn. Res., 2022)

Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization. - Agustinus Kristiadi; Alexander Immer; Runa Eschenhagen; Vincent Fortuin (CoRR, 2023)

Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference. - Kouroche Bouchiat; Alexander Immer; Hugo Yèche; Gunnar Rätsch; Vincent Fortuin (CoRR, 2023)

A Primer on Bayesian Neural Networks: Review and Debates. - Julyan Arbel; Konstantinos Pitas; Mariia Vladimirova; Vincent Fortuin (CoRR, 2023)

Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo. - Szilvia Ujváry; Gergely Flamich; Vincent Fortuin; José Miguel Hernández-Lobato (CoRR, 2023)

Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks. - Alexander Möllers; Alexander Immer; Elvin Isufi; Vincent Fortuin (CoRR, 2023)

Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice. - Jonas Rothfuss; Martin Josifoski; Vincent Fortuin; Andreas Krause 001 (J. Mach. Learn. Res., 2023)

Challenges and Perspectives in Deep Generative Modeling (Dagstuhl Seminar 23072). - Vincent Fortuin; Yingzhen Li; Kevin Murphy; Stephan Mandt; Laura Manduchi (Dagstuhl Reports, 2023)

Hodge-Aware Contrastive Learning. - Alexander Möllers; Alexander Immer; Vincent Fortuin; Elvin Isufi (ICASSP, 2024)

Improving Neural Additive Models with Bayesian Principles. - Kouroche Bouchiat; Alexander Immer; Hugo Yèche; Gunnar Rätsch; Vincent Fortuin (ICML, 2024)

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (ICML, 2024)

FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning. - Tristan Cinquin; Marvin Pförtner; Vincent Fortuin; Philipp Hennig; Robert Bamler (NeurIPS, 2024)

Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood. - Rayen Dhahri; Alexander Immer; Bertrand Charpentier; Stephan Günnemann; Vincent Fortuin (NeurIPS, 2024)

Understanding Pathologies of Deep Heteroskedastic Regression. - Eliot Wong-Toi; Alex Boyd; Vincent Fortuin; Stephan Mandt (UAI, 2024)

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (CoRR, 2024)

On the Challenges and Opportunities in Generative AI. - Laura Manduchi; Kushagra Pandey; Robert Bamler; Ryan Cotterell; Sina Däubener; Sophie Fellenz; Asja Fischer; Thomas Gärtner 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; Christoph Lippert; et al. (CoRR, 2024)

Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models. - Emre Onal; Klemens Flöge; Emma Caldwell; Arsen Sheverdin; Vincent Fortuin (CoRR, 2024)

How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization? - Agustinus Kristiadi; Felix Strieth-Kalthoff; Sriram Ganapathi Subramanian; Vincent Fortuin; Pascal Poupart; Geoff Pleiss (CoRR, 2024)

Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information. - Fedor Sergeev; Paola Malsot; Gunnar Rätsch; Vincent Fortuin (CoRR, 2024)

Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation. - Richard D. Paul; Alessio Quercia; Vincent Fortuin; Katharina Nöh; Hanno Scharr (CoRR, 2024)

Stein Variational Newton Neural Network Ensembles. - Klemens Flöge; Mohammed Abdul Moeed; Vincent Fortuin (CoRR, 2024)

OneProt: Towards Multi-Modal Protein Foundation Models. - Klemens Flöge; Srisruthi Udayakumar; Johanna Sommer; Marie Piraud; Stefan Kesselheim; Vincent Fortuin; Stephan Günnemann; Karel J. van der Weg; Holger Gohlke; Alina Bazarova; Erinc Merdivan (CoRR, 2024)

Rethinking the Role of Bayesianism in the Age of Modern AI (Dagstuhl Seminar 24461). - Vincent Fortuin; Mohammad Emtiyaz Khan; Mark van der Wilk; Zoubin Ghahramani; Katharine Fisher (Dagstuhl Reports, 2024)

Incorporating Unlabelled Data into Bayesian Neural Networks. - Mrinank Sharma; Tom Rainforth; Yee Whye Teh; Vincent Fortuin (Trans. Mach. Learn. Res., 2024)

Sparse Gaussian Neural Processes. - Tommy Rochussen; Vincent Fortuin (AABI, 2025)

Can Transformers Learn Full Bayesian Inference in Context? - Arik Reuter; Tim G. J. Rudner; Vincent Fortuin; David Rügamer (CoRR, 2025)

ProSpero: Active Learning for Robust Protein Design Beyond Wild-Type Neighborhoods. - Michal Kmicikiewicz; Vincent Fortuin; Ewa Szczurek (CoRR, 2025)