Publications

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

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Alexander Möllers, Alexander Immer, Vincent Fortuin, Elvin Isufi (2023). Hodge-Aware Contrastive Learning. arXiv.

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

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

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Eliot Wong-Toi, Alex Boyd, Vincent Fortuin, Stephan Mandt (2023). Understanding Pathologies of Deep Heteroskedastic Regression. arXiv.

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Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Rätsch, Vincent Fortuin (2023). Improving Neural Additive Models with Bayesian Principles. AABI.

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

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Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin (2023). Incorporating Unlabelled Data into Bayesian Neural Networks. arXiv.

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James Allingham, Florian Wenzel, Zelda Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, Vincent Fortuin, Balaji Lakshminarayanan, Jasper Snoek, Dustin Tran, Carlos Riquelme Ruiz, Rodolphe Jenatton (2022). Sparse MoEs meet Efficient Ensembles. TMLR.

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Noah Berner, Vincent Fortuin, Jonas Landman (2022). Quantum Bayesian Neural Networks. AABI.

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

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Vincent Fortuin (2022). Priors in Bayesian Deep Learning: A Review. International Statistical Review.

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Tristan Cinquin, Alexander Immer, Max Horn, Vincent Fortuin (2022). Pathologies in priors and inference for Bayesian transformers. AABI.

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

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

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Simon Bing, Vincent Fortuin, Gunnar Rätsch (2022). On Disentanglement in Gaussian Process Variational Autoencoders. AABI.

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Lauro Langosco di Langosco, Vincent Fortuin, Heiko Strathmann (2022). Neural Variational Gradient Descent. AABI.

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Marcello Massimo Negri, Vincent Fortuin, Jan Stuehmer (2022). Meta-learning richer priors for VAEs. AABI.

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

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Vincent Fortuin, Mark Collier, Florian Wenzel, James Allingham, Jeremiah Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou (2022). Deep Classifiers with Label Noise Modeling and Distance Awareness. TMLR.

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

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

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

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Laura Manduchi, Matthias Hüser, Martin Faltys, Julia Vogt, Gunnar Rätsch, Vincent Fortuin (2021). T-DPSOM: An Interpretable Clustering Method for Unsupervised Learning of Patient Health States. ACM CHIL.

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

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

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

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

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Alexandre Bense, Amir Joudaki, Tim GJ Rudner, Vincent Fortuin (2021). PCA Subspaces Are Not Always Optimal for Bayesian Learning. NeurIPS workshop on Distribution Shifts.

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

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Francesco D'Angelo, Vincent Fortuin, Florian Wenzel (2021). On Stein Variational Neural Network Ensembles. ICML workshop on Uncertainty and Robustness in Deep Learning.

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Andreas Kopf, Vincent Fortuin, Vignesh Ram Somnath, Manfred Claassen (2021). Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations. PLOS Computational Biology.

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Margherita Rosnati, Vincent Fortuin (2021). MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis. PLOS ONE.

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Metod Jazbec, Michael Pearce, Vincent Fortuin (2021). Factorized Gaussian Process Variational Autoencoders. AABI.

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

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

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

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Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos, Mark van der Wilk, Vincent Fortuin (2021). A Bayesian Approach to Invariant Deep Neural Networks. ICML workshop on Uncertainty and Robustness in Deep Learning.

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Matthew Ashman, Jonathan So, Will Tebbutt, Vincent Fortuin, Michael Pearce, Richard E Turner (2020). Sparse Gaussian Process Variational Autoencoders. arXiv.

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

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

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

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Andreas Georgiou, Vincent Fortuin, Harun Mustafa, Gunnar Rätsch (2019). META^2: Memory-efficient taxonomic classification and abundance estimation for metagenomics with deep learning. MLCB.

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Vincent Fortuin, Heiko Strathmann, Gunnar Rätsch (2019). Meta-Learning Mean Functions for Gaussian Processes. NeurIPS workshop on Bayesian Deep Learning.

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Laura Manduchi, Matthias Hüser, Gunnar Rätsch, Vincent Fortuin (2019). DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps. NeurIPS workshop on Machine Learning for Health.

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Stefan Ganscha, Vincent Fortuin, Max Horn, Eirini Arvaniti, Manfred Claassen (2018). Supervised learning on synthetic data for reverse engineering gene regulatory networks from experimental time-series. bioRxiv.

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Tim GJ Rudner, Vincent Fortuin, Yee Whye Teh, Yarin Gal (2018). On the Connection between Neural Processes and Gaussian Processes with Deep Kernels. NeurIPS workshop on Bayesian Deep Learning.

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

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