Vincent Fortuin

According to our database, Vincent Fortuin authored at least 64 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Incorporating Unlabelled Data into Bayesian Neural Networks.

Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin

Trans. Mach. Learn. Res., 2024

Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation.

Richard D. Paul, Alessio Quercia, Vincent Fortuin, Katharina Nöh, Hanno Scharr

CoRR, 2024

FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning.

Tristan Cinquin, Marvin Pförtner, Vincent Fortuin, Philipp Hennig, Robert Bamler

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

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

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

On the Challenges and Opportunities in Generative AI.

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

CoRR, 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

CoRR, 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, 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

CoRR, 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, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Improving Neural Additive Models with Bayesian Principles.

Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Hodge-Aware Contrastive Learning.

Alexander Möllers, Alexander Immer, Vincent Fortuin, Elvin Isufi

Proceedings of the IEEE International Conference on Acoustics, 2024

2023

Challenges and Perspectives in Deep Generative Modeling (Dagstuhl Seminar 23072).

Vincent Fortuin, Yingzhen Li, Kevin Murphy, Stephan Mandt, Laura Manduchi

Dagstuhl Reports, February, 2023

Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice.

Jonas Rothfuss, Martin Josifoski, Vincent Fortuin, Andreas Krause

J. Mach. Learn. Res., 2023

Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks.

Alexander Möllers, Alexander Immer, Elvin Isufi, 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

A Primer on Bayesian Neural Networks: Review and Debates.

Julyan Arbel, Konstantinos Pitas, Mariia Vladimirova, Vincent Fortuin

CoRR, 2023

Understanding Pathologies of Deep Heteroskedastic Regression.

Eliot Wong-Toi, Alex Boyd, Vincent Fortuin, Stephan Mandt

CoRR, 2023

Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference.

Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin

CoRR, 2023

Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization.

Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin

CoRR, 2023

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

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, Rodolphe Jenatton

Trans. Mach. Learn. Res., 2022

PAC-Bayesian Meta-Learning: From Theory to Practice.

Jonas Rothfuss, Martin Josifoski, Vincent Fortuin, Andreas Krause

CoRR, 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

Proceedings of the Uncertainty in Artificial Intelligence, 2022

On Interpretable Reranking-Based Dependency Parsing Systems.

Florian Schottmann, Vincent Fortuin, Edoardo M. Ponti, Ryan Cotterell

Proceedings of the Swiss Text Analytics Conference 2022, Lugano, 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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 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

Proceedings of the Tenth International Conference on Learning Representations, 2022

Probing as Quantifying Inductive Bias.

Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

2021

On the Choice of Priors in Bayesian Deep Learning.

Vincent Fortuin

PhD thesis, 2021

<i>BNNpriors</i>: 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

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

Probing as Quantifying the Inductive Bias of Pre-trained Representations.

Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell

CoRR, 2021

Pathologies in priors and inference for Bayesian transformers.

Tristan Cinquin, Alexander Immer, Max Horn, Vincent Fortuin

CoRR, 2021

Neural Variational Gradient Descent.

Lauro Langosco di Langosco, Vincent Fortuin, Heiko Strathmann

CoRR, 2021

A Bayesian Approach to Invariant Deep Neural Networks.

Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos, Mark van der Wilk, Vincent Fortuin

CoRR, 2021

On Stein Variational Neural Network Ensembles.

Francesco D'Angelo, Vincent Fortuin, Florian Wenzel

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

Priors in Bayesian Deep Learning: A Review.

Vincent Fortuin

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

On Disentanglement in Gaussian Process Variational Autoencoders.

Simon Bing, Vincent Fortuin, Gunnar Rätsch

CoRR, 2021

Exact Langevin Dynamics with Stochastic Gradients.

Adrià Garriga-Alonso, Vincent Fortuin

CoRR, 2021

Annealed Stein Variational Gradient Descent.

Francesco D'Angelo, Vincent Fortuin

CoRR, 2021

Sparse Gaussian Processes on Discrete Domains.

Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch

IEEE Access, 2021

Repulsive Deep Ensembles are Bayesian.

Francesco D'Angelo, Vincent Fortuin

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees.

Jonas Rothfuss, Vincent Fortuin, Martin Josifoski, Andreas Krause

Proceedings of the 38th International Conference on Machine Learning, 2021

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning.

Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, Mohammad Emtiyaz Khan

Proceedings of the 38th International Conference on Machine Learning, 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

Proceedings of the ACM CHIL '21: ACM Conference on Health, 2021

Scalable Gaussian Process Variational Autoencoders.

Metod Jazbec, Matthew Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Factorized Gaussian Process Variational Autoencoders.

Metod Jazbec, Michael Pearce, Vincent Fortuin

CoRR, 2020

Scalable Gaussian Process Variational Autoencoders.

Metod Jazbec, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch

CoRR, 2020

Sparse Gaussian Process Variational Autoencoders.

Matthew Ashman, Jonathan So, William Tebbutt, Vincent Fortuin, Michael Pearce, Richard E. Turner

CoRR, 2020

PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees.

Jonas Rothfuss, Vincent Fortuin, Andreas Krause

CoRR, 2020

Conservative Uncertainty Estimation By Fitting Prior Networks.

Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard E. Turner

Proceedings of the 8th International Conference on Learning Representations, 2020

GP-VAE: Deep Probabilistic Time Series Imputation.

Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, Stephan Mandt

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

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

Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps.

Laura Manduchi, Matthias Hüser, Gunnar Rätsch, 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

MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis.

Margherita Rosnati, Vincent Fortuin

CoRR, 2019

Multivariate Time Series Imputation with Variational Autoencoders.

Vincent Fortuin, Gunnar Rätsch, Stephan Mandt

CoRR, 2019

Deep Mean Functions for Meta-Learning in Gaussian Processes.

Vincent Fortuin, Gunnar Rätsch

CoRR, 2019

SOM-VAE: Interpretable Discrete Representation Learning on Time Series.

Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Scalable Gaussian Processes on Discrete Domains.

Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch

CoRR, 2018

Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series.

Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch

CoRR, 2018

InspireMe: Learning Sequence Models for Stories.

Vincent Fortuin, Romann M. Weber, Sasha Schriber, Diana Wotruba, Markus H. Gross

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018