Fortuin, Vincent

32 publications

ICML 2025 Can Transformers Learn Full Bayesian Inference in Context? Arik Reuter, Tim G. J. Rudner, Vincent Fortuin, David Rügamer

ICLRW 2025 Can Transformers Learn Full Bayesian Inference in Context? Arik Reuter, Tim G. J. Rudner, Vincent Fortuin, David Rügamer

TMLR 2025 On the Challenges and Opportunities in Generative AI 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 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

NeurIPS 2025 ProSpero: Active Learning for Robust Protein Design Beyond Wild-Type Neighborhoods Michal Kmicikiewicz, Vincent Fortuin, Ewa Szczurek

ICLRW 2025 What Actually Matters for Materials Discovery: Pitfalls and Recommendations in Bayesian Optimization Tristan Cinquin, Stanley Lo, Felix Strieth-Kalthoff, Alan Aspuru-Guzik, Geoff Pleiss, Robert Bamler, Tim G. J. Rudner, Vincent Fortuin, Agustinus Kristiadi

NeurIPS 2024 FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning Tristan Cinquin, Marvin Pförtner, Vincent Fortuin, Philipp Hennig, Robert Bamler

ICML 2024 Improving Neural Additive Models with Bayesian Principles Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Gunnar Ratsch, Vincent Fortuin

TMLR 2024 Incorporating Unlabelled Data into Bayesian Neural Networks Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin

ICML 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 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

NeurIPS 2024 Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood Rayen Dhahri, Alexander Immer, Betrand Charpentier, Stephan Günnemann, Vincent Fortuin

ICMLW 2024 Stein Variational Newton Neural Network Ensembles Klemens Flöge, Muhammad Abdul Moeed, Vincent Fortuin

ICMLW 2024 Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information Fedor Sergeev, Paola Malsot, Gunnar Ratsch, Vincent Fortuin

UAI 2024 Understanding Pathologies of Deep Heteroskedastic Regression Eliot Wong-Toi, Alex Boyd, Vincent Fortuin, Stephan Mandt

NeurIPSW 2023 Estimating Optimal PAC-Bayes Bounds with Hamiltonian Monte Carlo Szilvia Ujváry, Gergely Flamich, Vincent Fortuin, José Miguel Hernández-Lobato

JMLR 2023 Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice Jonas Rothfuss, Martin Josifoski, Vincent Fortuin, Andreas Krause

ICLR 2022 Bayesian Neural Network Priors Revisited Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Ratsch, Richard E Turner, Mark van der Wilk, Laurence Aitchison

UAI 2022 Data Augmentation in Bayesian Neural Networks and the Cold Posterior Effect Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso, Vincent Fortuin, Mark Wilk, Laurence Aitchison

TMLR 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

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

TMLR 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

AISTATS 2021 Scalable Gaussian Process Variational Autoencoders Metod Jazbec, Matt Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch

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

NeurIPSW 2021 PCA Subspaces Are Not Always Optimal for Bayesian Learning Alexandre Bense, Amir Joudaki, Tim G. J. Rudner, Vincent Fortuin

NeurIPSW 2021 Pathologies in Priors and Inference for Bayesian Transformers Tristan Cinquin, Alexander Immer, Max Horn, Vincent Fortuin

NeurIPS 2021 Repulsive Deep Ensembles Are Bayesian Francesco D'Angelo, Vincent Fortuin

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

NeurIPSW 2020 Bayesian Neural Network Priors Revisited Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Ratsch, Richard E Turner, Mark van der Wilk, Laurence Aitchison

ICLR 2020 Conservative Uncertainty Estimation by Fitting Prior Networks Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard Turner

AISTATS 2020 GP-VAE: Deep Probabilistic Time Series Imputation Vincent Fortuin, Dmitry Baranchuk, Gunnar Raetsch, Stephan Mandt

ICMLW 2020 PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees Jonas Rothfuss, Vincent Fortuin, Andreas Krause

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

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