Vincent Fortuin

91 papers A* 12A 4Misc 1Journal 67Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Amortising Inference and Meta-Learning Priors in Neural Networks. CoRR Tommy Rochussen, Vincent Fortuin
2026	J	`jnl`	In-Context Function Learning in Large Language Models. CoRR Elif Akata, Konstantinos Voudouris, Vincent Fortuin, Eric Schulz
2026	J	`jnl`	Standard Acquisition Is Sufficient for Asynchronous Bayesian Optimization. CoRR Ben Riegler, James Odgers, Vincent Fortuin
2025	J	`jnl`	Beyond Quantification: Navigating Uncertainty in Professional AI Systems. CoRR Sylvie Delacroix, Diana Robinson, Umang Bhatt, Jacopo Domenicucci, Jessica Montgomery, Gaël Varoquaux, Carl Henrik Ek, Vincent Fortuin, Yulan He, Tom Diethe, Neill W. Campbell, Mennatallah El-Assady, Søren Hauberg, Ivana Dusparic, Neil D. Lawrence
2025	A*	`conf`	Can Transformers Learn Full Bayesian Inference in Context? ICML Arik Reuter, Tim G. J. Rudner, Vincent Fortuin, David Rügamer
2025	J	`jnl`	Can Transformers Learn Full Bayesian Inference in Context? CoRR Arik Reuter, Tim G. J. Rudner, Vincent Fortuin, David Rügamer
2025	J	`jnl`	Data-Driven Discovery of Feature Groups in Clinical Time Series. CoRR Fedor Sergeev, Manuel Burger, Polina Leshetkina, Vincent Fortuin, Gunnar Rätsch, Rita Kuznetsova
2025	J	`jnl`	On the Challenges and Opportunities in Generative AI. Trans. Mach. Learn. Res. Laura Manduchi, Clara Meister, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric T. Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E. Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin
2025	J	`jnl`	On the Effect of Regularization on Nonparametric Mean-Variance Regression. CoRR Eliot Wong-Toi, Alex Boyd, Vincent Fortuin, Stephan Mandt
2025	J	`jnl`	OneProt: Towards multi-modal protein foundation models via latent space alignment of sequence, structure, binding sites and text encoders. PLoS Comput. Biol. Klemens Flöge, Srisruthi Udayakumar, Johanna Sommer, Marie Piraud, Stefan Kesselheim, Vincent Fortuin, Stephan Günnemann, Karel J. van der Weg, Holger Gohlke, Erinc Merdivan, Alina Bazarova
2025	J	`jnl`	ProSpero: Active Learning for Robust Protein Design Beyond Wild-Type Neighborhoods. CoRR Michal Kmicikiewicz, Vincent Fortuin, Ewa Szczurek
2025	—	`conf`	Sparse Gaussian Neural Processes. AABI Tommy Rochussen, Vincent Fortuin
2025	J	`jnl`	Sparse Gaussian Neural Processes. CoRR Tommy Rochussen, Vincent Fortuin
2024	A*	`conf`	FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning. NeurIPS Tristan Cinquin, Marvin Pförtner, Vincent Fortuin, Philipp Hennig, Robert Bamler
2024	J	`jnl`	FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning. CoRR Tristan Cinquin, Marvin Pförtner, Vincent Fortuin, Philipp Hennig, Robert Bamler
2024	J	`jnl`	Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models. CoRR Emre Onal, Klemens Flöge, Emma Caldwell, Arsen Sheverdin, Vincent Fortuin
2024	Misc	`conf`	Hodge-Aware Contrastive Learning. ICASSP Alexander Möllers, Alexander Immer, Vincent Fortuin, Elvin Isufi
2024	J	`jnl`	How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization? CoRR Agustinus Kristiadi, Felix Strieth-Kalthoff, Sriram Ganapathi Subramanian, Vincent Fortuin, Pascal Poupart, Geoff Pleiss
2024	A*	`conf`	Improving Neural Additive Models with Bayesian Principles. ICML Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin
2024	J	`jnl`	Incorporating Unlabelled Data into Bayesian Neural Networks. Trans. Mach. Learn. Res. Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin
2024	J	`jnl`	On the Challenges and Opportunities in Generative AI. CoRR Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric T. Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E. Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin
2024	J	`jnl`	OneProt: Towards Multi-Modal Protein Foundation Models. CoRR 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
2024	J	`jnl`	Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation. CoRR Richard D. Paul, Alessio Quercia, Vincent Fortuin, Katharina Nöh, Hanno Scharr
2024	J	`jnl`	Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR 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, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
2024	A*	`conf`	Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML 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, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
2024	J	`jnl`	Rethinking the Role of Bayesianism in the Age of Modern AI (Dagstuhl Seminar 24461). Dagstuhl Reports Vincent Fortuin, Mohammad Emtiyaz Khan, Mark van der Wilk, Zoubin Ghahramani, Katharine Fisher
2024	J	`jnl`	Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood. CoRR Rayen Dhahri, Alexander Immer, Bertrand Charpentier, Stephan Günnemann, Vincent Fortuin
2024	A*	`conf`	Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood. NeurIPS Rayen Dhahri, Alexander Immer, Bertrand Charpentier, Stephan Günnemann, Vincent Fortuin
2024	J	`jnl`	Stein Variational Newton Neural Network Ensembles. CoRR Klemens Flöge, Mohammed Abdul Moeed, Vincent Fortuin
2024	J	`jnl`	Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information. CoRR Fedor Sergeev, Paola Malsot, Gunnar Rätsch, Vincent Fortuin
2024	A	`conf`	Understanding Pathologies of Deep Heteroskedastic Regression. UAI Eliot Wong-Toi, Alex Boyd, Vincent Fortuin, Stephan Mandt
2023	J	`jnl`	A Primer on Bayesian Neural Networks: Review and Debates. CoRR Julyan Arbel, Konstantinos Pitas, Mariia Vladimirova, Vincent Fortuin
2023	J	`jnl`	Challenges and Perspectives in Deep Generative Modeling (Dagstuhl Seminar 23072). Dagstuhl Reports Vincent Fortuin, Yingzhen Li, Kevin Murphy, Stephan Mandt, Laura Manduchi
2023	J	`jnl`	Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo. CoRR Szilvia Ujváry, Gergely Flamich, Vincent Fortuin, José Miguel Hernández-Lobato
2023	J	`jnl`	Hodge-Aware Contrastive Learning. CoRR Alexander Möllers, Alexander Immer, Vincent Fortuin, Elvin Isufi
2023	J	`jnl`	Incorporating Unlabelled Data into Bayesian Neural Networks. CoRR Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin
2023	J	`jnl`	Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference. CoRR Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin
2023	—	`ed.`	Proceedings on "I Can't Believe It's Not Better: Failure Modes in the Age of Foundation Models" at NeurIPS 2023 Workshops, 16 December 2023, New Orleans, Louisiana, USA. ICBINB Javier Antorán, Arno Blaas, Kelly Buchanan, Fan Feng, Vincent Fortuin, Sahra Ghalebikesabi, Andreas Kriegler, Ian Mason, David Rohde, Francisco J. R. Ruiz, Tobias Uelwer, Yubin Xie, Rui Yang
2023	J	`jnl`	Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization. CoRR Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin
2023	J	`jnl`	Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice. J. Mach. Learn. Res. Jonas Rothfuss, Martin Josifoski, Vincent Fortuin, Andreas Krause
2023	J	`jnl`	Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks. CoRR Alexander Möllers, Alexander Immer, Elvin Isufi, Vincent Fortuin
2023	J	`jnl`	Understanding Pathologies of Deep Heteroskedastic Regression. CoRR Eliot Wong-Toi, Alex Boyd, Vincent Fortuin, Stephan Mandt
2022	A*	`conf`	Bayesian Neural Network Priors Revisited. ICLR Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison
2022	A	`conf`	Data augmentation in Bayesian neural networks and the cold posterior effect. UAI Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso, Vincent Fortuin, Mark van der Wilk, Laurence Aitchison
2022	J	`jnl`	Deep Classifiers with Label Noise Modeling and Distance Awareness. Trans. Mach. Learn. Res. Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Zhe Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
2022	A*	`conf`	Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. NeurIPS Alexander Immer, Tycho F. A. van der Ouderaa, Gunnar Rätsch, Vincent Fortuin, Mark van der Wilk
2022	J	`jnl`	Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. CoRR Alexander Immer, Tycho F. A. van der Ouderaa, Vincent Fortuin, Gunnar Rätsch, Mark van der Wilk
2022	—	`conf`	On Interpretable Reranking-Based Dependency Parsing Systems. SwissText Florian Schottmann, Vincent Fortuin, Edoardo M. Ponti, Ryan Cotterell
2022	J	`jnl`	PAC-Bayesian Meta-Learning: From Theory to Practice. CoRR Jonas Rothfuss, Martin Josifoski, Vincent Fortuin, Andreas Krause
2022	—	`conf`	Probing as Quantifying Inductive Bias. ACL (1) Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell
2022	J	`jnl`	Sparse MoEs meet Efficient Ensembles. Trans. Mach. Learn. Res. 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, Rodolphe Jenatton
2021	J	`jnl`	A Bayesian Approach to Invariant Deep Neural Networks. CoRR Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos, Mark van der Wilk, Vincent Fortuin
2021	J	`jnl`	Annealed Stein Variational Gradient Descent. CoRR Francesco D'Angelo, Vincent Fortuin
2021	J	`jnl`	BNNpriors: A library for Bayesian neural network inference with different prior distributions. CoRR Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk, Laurence Aitchison
2021	J	`jnl`	Bayesian Neural Network Priors Revisited. CoRR Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison
2021	J	`jnl`	Data augmentation in Bayesian neural networks and the cold posterior effect. CoRR Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso, Vincent Fortuin, Mark van der Wilk, Laurence Aitchison
2021	J	`jnl`	Deep Classifiers with Label Noise Modeling and Distance Awareness. CoRR Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Z. Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
2021	J	`jnl`	Exact Langevin Dynamics with Stochastic Gradients. CoRR Adrià Garriga-Alonso, Vincent Fortuin
2021	J	`jnl`	Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data. PLoS Comput. Biol. Andreas Kopf, Vincent Fortuin, Vignesh Ram Somnath, Manfred Claassen
2021	J	`jnl`	Neural Variational Gradient Descent. CoRR Lauro Langosco di Langosco, Vincent Fortuin, Heiko Strathmann
2021	J	`jnl`	On Disentanglement in Gaussian Process Variational Autoencoders. CoRR Simon Bing, Vincent Fortuin, Gunnar Rätsch
2021	J	`jnl`	On Stein Variational Neural Network Ensembles. CoRR Francesco D'Angelo, Vincent Fortuin, Florian Wenzel
2021	—	`—`	On the Choice of Priors in Bayesian Deep Learning. Vincent Fortuin
2021	A*	`conf`	PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees. ICML Jonas Rothfuss, Vincent Fortuin, Martin Josifoski, Andreas Krause
2021	J	`jnl`	Pathologies in priors and inference for Bayesian transformers. CoRR Tristan Cinquin, Alexander Immer, Max Horn, Vincent Fortuin
2021	J	`jnl`	Priors in Bayesian Deep Learning: A Review. CoRR Vincent Fortuin
2021	J	`jnl`	Probing as Quantifying the Inductive Bias of Pre-trained Representations. CoRR Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell
2021	A*	`conf`	Repulsive Deep Ensembles are Bayesian. NeurIPS Francesco D'Angelo, Vincent Fortuin
2021	J	`jnl`	Repulsive Deep Ensembles are Bayesian. CoRR Francesco D'Angelo, Vincent Fortuin
2021	A	`conf`	Scalable Gaussian Process Variational Autoencoders. AISTATS Metod Jazbec, Matthew Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch
2021	A*	`conf`	Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. ICML Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan
2021	J	`jnl`	Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. CoRR Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan
2021	J	`jnl`	Sparse Gaussian Processes on Discrete Domains. IEEE Access Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch
2021	J	`jnl`	Sparse MoEs meet Efficient Ensembles. CoRR 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, Rodolphe Jenatton
2021	—	`conf`	T-DPSOM: an interpretable clustering method for unsupervised learning of patient health states. CHIL Laura Manduchi, Matthias Hüser, Martin Faltys, Julia E. Vogt, Gunnar Rätsch, Vincent Fortuin
2020	A*	`conf`	Conservative Uncertainty Estimation By Fitting Prior Networks. ICLR Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard E. Turner
2020	J	`jnl`	Factorized Gaussian Process Variational Autoencoders. CoRR Metod Jazbec, Michael Pearce, Vincent Fortuin
2020	A	`conf`	GP-VAE: Deep Probabilistic Time Series Imputation. AISTATS Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, Stephan Mandt
2020	J	`jnl`	PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees. CoRR Jonas Rothfuss, Vincent Fortuin, Andreas Krause
2020	J	`jnl`	Scalable Gaussian Process Variational Autoencoders. CoRR Metod Jazbec, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch
2020	J	`jnl`	Sparse Gaussian Process Variational Autoencoders. CoRR Matthew Ashman, Jonathan So, William Tebbutt, Vincent Fortuin, Michael Pearce, Richard E. Turner
2019	J	`jnl`	Deep Mean Functions for Meta-Learning in Gaussian Processes. CoRR Vincent Fortuin, Gunnar Rätsch
2019	J	`jnl`	Deep Multiple Instance Learning for Taxonomic Classification of Metagenomic read sets. CoRR Andreas Georgiou, Vincent Fortuin, Harun Mustafa, Gunnar Rätsch
2019	J	`jnl`	MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis. CoRR Margherita Rosnati, Vincent Fortuin
2019	J	`jnl`	Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations. CoRR Andreas Kopf, Vincent Fortuin, Vignesh Ram Somnath, Manfred Claassen
2019	J	`jnl`	Multivariate Time Series Imputation with Variational Autoencoders. CoRR Vincent Fortuin, Gunnar Rätsch, Stephan Mandt
2019	—	`conf`	SOM-VAE: Interpretable Discrete Representation Learning on Time Series. ICLR (Poster) Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch
2019	J	`jnl`	Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps. CoRR Laura Manduchi, Matthias Hüser, Gunnar Rätsch, Vincent Fortuin
2018	J	`jnl`	Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series. CoRR Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch
2018	A*	`conf`	InspireMe: Learning Sequence Models for Stories. AAAI Vincent Fortuin, Romann M. Weber, Sasha Schriber, Diana Wotruba, Markus H. Gross
2018	J	`jnl`	Scalable Gaussian Processes on Discrete Domains. CoRR Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch