Vincent Fortuin
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
15
Venues
7
Active years
2018–2025
Best venue rank
A*
Where they publish
Papers
15 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ICML | Can Transformers Learn Full Bayesian Inference in Context? | Arik Reuter, Tim G. J. Rudner, Vincent Fortuin, David Rgamer |
| 2024 | ICASSP | Hodge-Aware Contrastive Learning. | Alexander Mllers, Alexander Immer, Vincent Fortuin, Elvin Isufi |
| 2024 | ICML | Improving Neural Additive Models with Bayesian Principles. | Kouroche Bouchiat, Alexander Immer, Hugo Yche, Gunnar Rtsch, Vincent Fortuin |
| 2024 | ICML | 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, Jos Miguel Hernndez-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 Rgamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang |
| 2024 | UAI | Understanding Pathologies of Deep Heteroskedastic Regression. | Eliot Wong-Toi, Alex Boyd, Vincent Fortuin, Stephan Mandt |
| 2022 | ACL | Probing as Quantifying Inductive Bias. | Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, Ryan Cotterell |
| 2022 | ICLR | Bayesian Neural Network Priors Revisited. | Vincent Fortuin, Adri Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Rtsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison |
| 2022 | UAI | Data augmentation in Bayesian neural networks and the cold posterior effect. | Seth Nabarro, Stoil Ganev, Adri Garriga-Alonso, Vincent Fortuin, Mark van der Wilk, Laurence Aitchison |
| 2021 | AISTATS | Scalable Gaussian Process Variational Autoencoders. | Metod Jazbec, Matthew Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rtsch |
| 2021 | ICML | Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning. | Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rtsch, Mohammad Emtiyaz Khan |
| 2021 | ICML | PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees. | Jonas Rothfuss, Vincent Fortuin, Martin Josifoski, Andreas Krause |
| 2020 | AISTATS | GP-VAE: Deep Probabilistic Time Series Imputation. | Vincent Fortuin, Dmitry Baranchuk, Gunnar Rtsch, Stephan Mandt |
| 2020 | ICLR | Conservative Uncertainty Estimation By Fitting Prior Networks. | Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard E. Turner |
| 2019 | ICLR | SOM-VAE: Interpretable Discrete Representation Learning on Time Series. | Vincent Fortuin, Matthias Hser, Francesco Locatello, Heiko Strathmann, Gunnar Rtsch |
| 2018 | AAAI | InspireMe: Learning Sequence Models for Stories. | Vincent Fortuin, Romann M. Weber, Sasha Schriber, Diana Wotruba, Markus H. Gross |