Vincent Fortuin

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 67

Top Topics

Prior Distribution

Top Venues

Trans. Mach. Learn. Res.

Publications

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 (2024)
Fedor Sergeev, Paola Malsot, Gunnar Rätsch, Vincent Fortuin
Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information. CoRR (2024)
Alexander Möllers, Alexander Immer, Vincent Fortuin, Elvin Isufi
Hodge-Aware Contrastive Learning. ICASSP (2024)
Tristan Cinquin, Marvin Pförtner, Vincent Fortuin, Philipp Hennig, Robert Bamler
FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning. CoRR (2024)
Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin
Incorporating Unlabelled Data into Bayesian Neural Networks. Trans. Mach. Learn. Res. 2024 (2024)
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 (2024)
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 (2024)
Emre Onal, Klemens Flöge, Emma Caldwell, Arsen Sheverdin, Vincent Fortuin
Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models. CoRR (2024)
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 (2024)
Eliot Wong-Toi, Alex Boyd, Vincent Fortuin, Stephan Mandt
Understanding Pathologies of Deep Heteroskedastic Regression. CoRR (2023)
Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin
Incorporating Unlabelled Data into Bayesian Neural Networks. CoRR (2023)
Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization. CoRR (2023)
Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin
Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference. CoRR (2023)
Julyan Arbel, Konstantinos Pitas, Mariia Vladimirova, Vincent Fortuin
A Primer on Bayesian Neural Networks: Review and Debates. CoRR (2023)
Alexander Möllers, Alexander Immer, Elvin Isufi, Vincent Fortuin
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks. CoRR (2023)
Vincent Fortuin, Yingzhen Li, Kevin Murphy, Stephan Mandt, Laura Manduchi
Challenges and Perspectives in Deep Generative Modeling (Dagstuhl Seminar 23072). Dagstuhl Reports 13 (2) (2023)
Szilvia Ujváry, Gergely Flamich, Vincent Fortuin, José Miguel Hernández-Lobato
Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo. CoRR (2023)
Alexander Möllers, Alexander Immer, Vincent Fortuin, Elvin Isufi
Hodge-Aware Contrastive Learning. CoRR (2023)
Jonas Rothfuss, Martin Josifoski, Vincent Fortuin, Andreas Krause
PAC-Bayesian Meta-Learning: From Theory to Practice. CoRR (2022)
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)
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 (2022)
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)
Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell
Probing as Quantifying Inductive Bias. ACL (1) (2022)
Florian Schottmann, Vincent Fortuin, Edoardo M. Ponti, Ryan Cotterell
On Interpretable Reranking-Based Dependency Parsing Systems. SwissText (2022)
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)
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)
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)
Adrià Garriga-Alonso, Vincent Fortuin
Exact Langevin Dynamics with Stochastic Gradients. CoRR (2021)
Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch
Sparse Gaussian Processes on Discrete Domains. IEEE Access 9 (2021)
Francesco D'Angelo, Vincent Fortuin
Repulsive Deep Ensembles are Bayesian. NeurIPS (2021)
Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. ICML (2021)
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)
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)
Tristan Cinquin, Alexander Immer, Max Horn, Vincent Fortuin
Pathologies in priors and inference for Bayesian transformers. CoRR (2021)
Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell
Probing as Quantifying the Inductive Bias of Pre-trained Representations. CoRR (2021)
Lauro Langosco di Langosco, Vincent Fortuin, Heiko Strathmann
Neural Variational Gradient Descent. CoRR (2021)
Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison
Bayesian Neural Network Priors Revisited. CoRR (2021)
Jonas Rothfuss, Vincent Fortuin, Martin Josifoski, Andreas Krause
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees. ICML (2021)
Vincent Fortuin
Priors in Bayesian Deep Learning: A Review. CoRR (2021)
Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. CoRR (2021)
Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk, Laurence Aitchison
BNNpriors: A library for Bayesian neural network inference with different prior distributions. CoRR (2021)
Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
Deep Classifiers with Label Noise Modeling and Distance Awareness. CoRR (2021)
Francesco D'Angelo, Vincent Fortuin, Florian Wenzel
On Stein Variational Neural Network Ensembles. CoRR (2021)
Francesco D'Angelo, Vincent Fortuin
Repulsive Deep Ensembles are Bayesian. CoRR (2021)
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 (2021)
Simon Bing, Vincent Fortuin, Gunnar Rätsch
On Disentanglement in Gaussian Process Variational Autoencoders. CoRR (2021)
Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk, Laurence Aitchison
: A library for Bayesian neural network inference with different prior distributions. Softw. Impacts 9 (2021)
Metod Jazbec, Matthew Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch
Scalable Gaussian Process Variational Autoencoders. AISTATS (2021)
Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos, Mark van der Wilk, Vincent Fortuin
A Bayesian Approach to Invariant Deep Neural Networks. CoRR (2021)
Francesco D'Angelo, Vincent Fortuin
Annealed Stein Variational Gradient Descent. CoRR (2021)
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 (2021)
Metod Jazbec, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch
Scalable Gaussian Process Variational Autoencoders. CoRR (2020)
Jonas Rothfuss, Vincent Fortuin, Andreas Krause
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees. CoRR (2020)
Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, Stephan Mandt
GP-VAE: Deep Probabilistic Time Series Imputation. AISTATS (2020)
Metod Jazbec, Michael Pearce, Vincent Fortuin
Factorized Gaussian Process Variational Autoencoders. CoRR (2020)
Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard E. Turner
Conservative Uncertainty Estimation By Fitting Prior Networks. ICLR (2020)
Matthew Ashman, Jonathan So, William Tebbutt, Vincent Fortuin, Michael Pearce, Richard E. Turner
Sparse Gaussian Process Variational Autoencoders. CoRR (2020)
Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch
SOM-VAE: Interpretable Discrete Representation Learning on Time Series. ICLR (Poster) (2019)
Andreas Georgiou, Vincent Fortuin, Harun Mustafa, Gunnar Rätsch
Deep Multiple Instance Learning for Taxonomic Classification of Metagenomic read sets. CoRR (2019)
Laura Manduchi, Matthias Hüser, Gunnar Rätsch, Vincent Fortuin
Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps. CoRR (2019)
Vincent Fortuin, Gunnar Rätsch, Stephan Mandt
Multivariate Time Series Imputation with Variational Autoencoders. CoRR (2019)
Vincent Fortuin, Gunnar Rätsch
Deep Mean Functions for Meta-Learning in Gaussian Processes. CoRR (2019)
Andreas Kopf, Vincent Fortuin, Vignesh Ram Somnath, Manfred Claassen
Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations. CoRR (2019)
Margherita Rosnati, Vincent Fortuin
MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis. CoRR (2019)
Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch
Scalable Gaussian Processes on Discrete Domains. CoRR (2018)
Vincent Fortuin, Romann M. Weber, Sasha Schriber, Diana Wotruba, Markus H. Gross
InspireMe: Learning Sequence Models for Stories. AAAI (2018)
Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch
Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series. CoRR (2018)