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ORCIDVincent Fortuin
Person information
- affiliation: Helmholtz AI, Munich, Germany
- affiliation (former): ETH Zurich, Switzerland
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2020 – today
- 2024
[j7]Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin:
Incorporating Unlabelled Data into Bayesian Neural Networks. Trans. Mach. Learn. Res. 2024 (2024)- [c17]Alexander Möllers, Alexander Immer, Vincent Fortuin, Elvin Isufi:
Hodge-Aware Contrastive Learning. ICASSP 2024: 9746-9750 - [c16]Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin:
Improving Neural Additive Models with Bayesian Principles. ICML 2024 - [c15]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:
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML 2024 - [i47]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:
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR abs/2402.00809 (2024) - [i46]Rayen Dhahri, Alexander Immer, Bertrand Charpentier, Stephan Günnemann, Vincent Fortuin:
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood. CoRR abs/2402.15978 (2024) - [i45]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:
On the Challenges and Opportunities in Generative AI. CoRR abs/2403.00025 (2024) - [i44]Emre Onal, Klemens Flöge, Emma Caldwell, Arsen Sheverdin, Vincent Fortuin:
Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models. CoRR abs/2405.03425 (2024) - [i43]Agustinus Kristiadi, Felix Strieth-Kalthoff, Sriram Ganapathi Subramanian, Vincent Fortuin, Pascal Poupart, Geoff Pleiss:
How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization? CoRR abs/2406.06459 (2024) - [i42]Fedor Sergeev, Paola Malsot, Gunnar Rätsch, Vincent Fortuin:
Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information. CoRR abs/2407.13429 (2024) - [i41]Tristan Cinquin, Marvin Pförtner, Vincent Fortuin, Philipp Hennig, Robert Bamler:
FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning. CoRR abs/2407.13711 (2024) - [i40]Richard D. Paul, Alessio Quercia, Vincent Fortuin, Katharina Nöh, Hanno Scharr:
Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation. CoRR abs/2409.17085 (2024) - 2023
- [j6]Jonas Rothfuss, Martin Josifoski, Vincent Fortuin, Andreas Krause:
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice. J. Mach. Learn. Res. 24: 386:1-386:62 (2023) - [e1]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:
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. Proceedings of Machine Learning Research 239, PMLR 2023 [contents] - [i39]Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin:
Incorporating Unlabelled Data into Bayesian Neural Networks. CoRR abs/2304.01762 (2023) - [i38]Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin:
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization. CoRR abs/2304.08309 (2023) - [i37]Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin:
Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference. CoRR abs/2305.16905 (2023) - [i36]Eliot Wong-Toi, Alex Boyd, Vincent Fortuin, Stephan Mandt:
Understanding Pathologies of Deep Heteroskedastic Regression. CoRR abs/2306.16717 (2023) - [i35]Alexander Möllers, Alexander Immer, Vincent Fortuin, Elvin Isufi:
Hodge-Aware Contrastive Learning. CoRR abs/2309.07364 (2023) - [i34]Julyan Arbel, Konstantinos Pitas, Mariia Vladimirova, Vincent Fortuin:
A Primer on Bayesian Neural Networks: Review and Debates. CoRR abs/2309.16314 (2023) - [i33]Szilvia Ujváry, Gergely Flamich, Vincent Fortuin, José Miguel Hernández-Lobato:
Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo. CoRR abs/2310.20053 (2023) - [i32]Alexander Möllers, Alexander Immer, Elvin Isufi, Vincent Fortuin:
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks. CoRR abs/2312.00232 (2023) - [i31]Vincent Fortuin, Yingzhen Li, Kevin Murphy, Stephan Mandt, Laura Manduchi:
Challenges and Perspectives in Deep Generative Modeling (Dagstuhl Seminar 23072). Dagstuhl Reports 13(2): 47-70 (2023) - 2022
- [j5]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:
Sparse MoEs meet Efficient Ensembles. Trans. Mach. Learn. Res. 2022 (2022) - [j4]Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Zhe Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou:
Deep Classifiers with Label Noise Modeling and Distance Awareness. Trans. Mach. Learn. Res. 2022 (2022) - [c14]Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell:
Probing as Quantifying Inductive Bias. ACL (1) 2022: 1839-1851 - [c13]Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison:
Bayesian Neural Network Priors Revisited. ICLR 2022 - [c12]Alexander Immer, Tycho F. A. van der Ouderaa, Gunnar Rätsch, Vincent Fortuin, Mark van der Wilk:
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. NeurIPS 2022 - [c11]Florian Schottmann, Vincent Fortuin, Edoardo M. Ponti, Ryan Cotterell:
On Interpretable Reranking-Based Dependency Parsing Systems. SwissText 2022: 23-28 - [c10]Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso, Vincent Fortuin, Mark van der Wilk, Laurence Aitchison:
Data augmentation in Bayesian neural networks and the cold posterior effect. UAI 2022: 1434-1444 - [i30]Alexander Immer, Tycho F. A. van der Ouderaa, Vincent Fortuin, Gunnar Rätsch, Mark van der Wilk:
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations. CoRR abs/2202.10638 (2022) - [i29]Jonas Rothfuss, Martin Josifoski, Vincent Fortuin, Andreas Krause:
PAC-Bayesian Meta-Learning: From Theory to Practice. CoRR abs/2211.07206 (2022) - 2021
- [b1]Vincent Fortuin:
On the Choice of Priors in Bayesian Deep Learning. ETH Zurich, Zürich, Switzerland, 2021 - [j3]Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch:
Sparse Gaussian Processes on Discrete Domains. IEEE Access 9: 76750-76758 (2021) - [j2]Andreas Kopf, Vincent Fortuin, Vignesh Ram Somnath, Manfred Claassen:
Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data. PLoS Comput. Biol. 17(6) (2021) - [j1]Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk, Laurence Aitchison:
BNNpriors: A library for Bayesian neural network inference with different prior distributions. Softw. Impacts 9: 100079 (2021) - [c9]Metod Jazbec, Matthew Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch:
Scalable Gaussian Process Variational Autoencoders. AISTATS 2021: 3511-3519 - [c8]Laura Manduchi, Matthias Hüser, Martin Faltys, Julia E. Vogt, Gunnar Rätsch, Vincent Fortuin:
T-DPSOM: an interpretable clustering method for unsupervised learning of patient health states. CHIL 2021: 236-245 - [c7]Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan:
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. ICML 2021: 4563-4573 - [c6]Jonas Rothfuss, Vincent Fortuin, Martin Josifoski, Andreas Krause:
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees. ICML 2021: 9116-9126 - [c5]Francesco D'Angelo, Vincent Fortuin:
Repulsive Deep Ensembles are Bayesian. NeurIPS 2021: 3451-3465 - [i28]Francesco D'Angelo, Vincent Fortuin:
Annealed Stein Variational Gradient Descent. CoRR abs/2101.09815 (2021) - [i27]Adrià Garriga-Alonso, Vincent Fortuin:
Exact Langevin Dynamics with Stochastic Gradients. CoRR abs/2102.01691 (2021) - [i26]Simon Bing, Vincent Fortuin, Gunnar Rätsch:
On Disentanglement in Gaussian Process Variational Autoencoders. CoRR abs/2102.05507 (2021) - [i25]Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison:
Bayesian Neural Network Priors Revisited. CoRR abs/2102.06571 (2021) - [i24]Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan:
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. CoRR abs/2104.04975 (2021) - [i23]Vincent Fortuin:
Priors in Bayesian Deep Learning: A Review. CoRR abs/2105.06868 (2021) - [i22]Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk, Laurence Aitchison:
BNNpriors: A library for Bayesian neural network inference with different prior distributions. CoRR abs/2105.06964 (2021) - [i21]Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso, Vincent Fortuin, Mark van der Wilk, Laurence Aitchison:
Data augmentation in Bayesian neural networks and the cold posterior effect. CoRR abs/2106.05586 (2021) - [i20]Francesco D'Angelo, Vincent Fortuin, Florian Wenzel:
On Stein Variational Neural Network Ensembles. CoRR abs/2106.10760 (2021) - [i19]Francesco D'Angelo, Vincent Fortuin:
Repulsive Deep Ensembles are Bayesian. CoRR abs/2106.11642 (2021) - [i18]Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos, Mark van der Wilk, Vincent Fortuin:
A Bayesian Approach to Invariant Deep Neural Networks. CoRR abs/2107.09301 (2021) - [i17]Lauro Langosco di Langosco, Vincent Fortuin, Heiko Strathmann:
Neural Variational Gradient Descent. CoRR abs/2107.10731 (2021) - [i16]Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Z. Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou:
Deep Classifiers with Label Noise Modeling and Distance Awareness. CoRR abs/2110.02609 (2021) - [i15]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:
Sparse MoEs meet Efficient Ensembles. CoRR abs/2110.03360 (2021) - [i14]Tristan Cinquin, Alexander Immer, Max Horn, Vincent Fortuin:
Pathologies in priors and inference for Bayesian transformers. CoRR abs/2110.04020 (2021) - [i13]Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell:
Probing as Quantifying the Inductive Bias of Pre-trained Representations. CoRR abs/2110.08388 (2021) - 2020
- [c4]Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, Stephan Mandt:
GP-VAE: Deep Probabilistic Time Series Imputation. AISTATS 2020: 1651-1661 - [c3]Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard E. Turner:
Conservative Uncertainty Estimation By Fitting Prior Networks. ICLR 2020 - [i12]Jonas Rothfuss, Vincent Fortuin, Andreas Krause:
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees. CoRR abs/2002.05551 (2020) - [i11]Matthew Ashman, Jonathan So, William Tebbutt, Vincent Fortuin, Michael Pearce, Richard E. Turner:
Sparse Gaussian Process Variational Autoencoders. CoRR abs/2010.10177 (2020) - [i10]Metod Jazbec, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch:
Scalable Gaussian Process Variational Autoencoders. CoRR abs/2010.13472 (2020) - [i9]Metod Jazbec, Michael Pearce, Vincent Fortuin:
Factorized Gaussian Process Variational Autoencoders. CoRR abs/2011.07255 (2020)
2010 – 2019
- 2019
- [c2]Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch:
SOM-VAE: Interpretable Discrete Representation Learning on Time Series. ICLR (Poster) 2019 - [i8]Vincent Fortuin, Gunnar Rätsch:
Deep Mean Functions for Meta-Learning in Gaussian Processes. CoRR abs/1901.08098 (2019) - [i7]Vincent Fortuin, Gunnar Rätsch, Stephan Mandt:
Multivariate Time Series Imputation with Variational Autoencoders. CoRR abs/1907.04155 (2019) - [i6]Margherita Rosnati, Vincent Fortuin:
MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis. CoRR abs/1909.12637 (2019) - [i5]Andreas Georgiou, Vincent Fortuin, Harun Mustafa, Gunnar Rätsch:
Deep Multiple Instance Learning for Taxonomic Classification of Metagenomic read sets. CoRR abs/1909.13146 (2019) - [i4]Laura Manduchi, Matthias Hüser, Gunnar Rätsch, Vincent Fortuin:
Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps. CoRR abs/1910.01590 (2019) - [i3]Andreas Kopf, Vincent Fortuin, Vignesh Ram Somnath, Manfred Claassen:
Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations. CoRR abs/1910.07763 (2019) - 2018
- [c1]Vincent Fortuin, Romann M. Weber, Sasha Schriber, Diana Wotruba, Markus H. Gross:
InspireMe: Learning Sequence Models for Stories. AAAI 2018: 7747-7752 - [i2]Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch:
Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series. CoRR abs/1806.02199 (2018) - [i1]Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch:
Scalable Gaussian Processes on Discrete Domains. CoRR abs/1810.10368 (2018)
Coauthor Index
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