Data-Driven Discovery of Feature Groups in Clinical Time Series

Fedor Sergeev, Manuel Burger, Polina Leshetkina +3

Factorized Gaussian Process Variational Autoencoders

Metod Jazbec, Michael Pearce, Vincent Fortuin

Repulsive Deep Ensembles are Bayesian

Francesco D'Angelo, Vincent Fortuin

On Disentanglement in Gaussian Process Variational Autoencoders

Simon Bing, Vincent Fortuin, Gunnar Rätsch

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

Theodore Papamarkou, Maria Skoularidou, Konstantina Palla +22

Scalable Gaussian Processes on Discrete Domains

Vincent Fortuin, Gideon Dresdner, Heiko Strathmann +1

Towards Dynamic Feature Acquisition on Medical Time Series by Maximizing Conditional Mutual Information

Fedor Sergeev, Paola Malsot, Gunnar Rätsch +1

Decision-Aligned Evaluation of Uncertainty Quantification

Annika Schneider, Tommy Rochussen, Joshua Stiller +1

OneProt: Towards Multi-Modal Protein Foundation Models

Klemens Flöge, Srisruthi Udayakumar, Johanna Sommer +8

DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps

Laura Manduchi, Matthias Hüser, Julia Vogt +2

Stein Variational Newton Neural Network Ensembles

Klemens Flöge, Mohammed Abdul Moeed, Vincent Fortuin

Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations

Alexander Immer, Tycho F. A. van der Ouderaa, Gunnar Rätsch +2

Sparse Gaussian Neural Processes

Tommy Rochussen, Vincent Fortuin

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

Tristan Cinquin, Marvin Pförtner, Vincent Fortuin +2

Quantum Bayesian Neural Networks

Noah Berner, Vincent Fortuin, Jonas Landman

Annealed Stein Variational Gradient Descent

Francesco D'Angelo, Vincent Fortuin

Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks

Alexander Möllers, Alexander Immer, Elvin Isufi +1

Amortising Inference and Meta-Learning Priors in Neural Networks

Tommy Rochussen, Vincent Fortuin

SOM-VAE: Interpretable Discrete Representation Learning on Time Series

Vincent Fortuin, Matthias Hüser, Francesco Locatello +2

META${}^2$: Memory-efficient taxonomic classification and abundance estimation for metagenomics with deep learning

Andreas Georgiou, Vincent Fortuin, Harun Mustafa +1

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

Jonas Rothfuss, Vincent Fortuin, Martin Josifoski +1

On the Effect of Regularization on Nonparametric Mean-Variance Regression

Eliot Wong-Toi, Alex Boyd, Vincent Fortuin +1

Can Transformers Learn Full Bayesian Inference in Context?

Arik Reuter, Tim G. J. Rudner, Vincent Fortuin +1

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

Richard D. Paul, Alessio Quercia, Vincent Fortuin +2

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning

Alexander Immer, Matthias Bauer, Vincent Fortuin +2

Understanding Pathologies of Deep Heteroskedastic Regression

Eliot Wong-Toi, Alex Boyd, Vincent Fortuin +1

Pathologies in priors and inference for Bayesian transformers

Tristan Cinquin, Alexander Immer, Max Horn +1

Bayesian Neural Network Priors Revisited

Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober +5

Beyond Quantification: Navigating Uncertainty in Professional AI Systems

Sylvie Delacroix, Diana Robinson, Umang Bhatt +12

Position: agentic AI orchestration should be Bayes-consistent

Theodore Papamarkou, Pierre Alquier, Matthias Bauer +27

Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models

Emre Onal, Klemens Flöge, Emma Caldwell +2

Data augmentation in Bayesian neural networks and the cold posterior effect

Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso +3

Sparse Gaussian Process Variational Autoencoders

Matthew Ashman, Jonathan So, Will Tebbutt +3

On the Challenges and Opportunities in Generative AI

Laura Manduchi, Clara Meister, Kushagra Pandey +23

Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell Data

Andreas Kopf, Vincent Fortuin, Vignesh Ram Somnath +1

Gaussian Mean Field Variational Inference can Overestimate Predictive Variance

James Odgers, Ben Riegler, Siddharth Swaroop +1

Incorporating Unlabelled Data into Bayesian Neural Networks

Mrinank Sharma, Tom Rainforth, Yee Whye Teh +1

Deep Classifiers with Label Noise Modeling and Distance Awareness

Vincent Fortuin, Mark Collier, Florian Wenzel +7

Improving Neural Additive Models with Bayesian Principles

Kouroche Bouchiat, Alexander Immer, Hugo Yèche +2

A Primer on Bayesian Neural Networks: Review and Debates

Julyan Arbel, Konstantinos Pitas, Mariia Vladimirova +1

Priors in Bayesian Deep Learning: A Review

Vincent Fortuin

A Bayesian Approach to Invariant Deep Neural Networks

Nikolaos Mourdoukoutas, Marco Federici, Georges Pantalos +2

ProSpero: Active Learning for Robust Protein Design Beyond Wild-Type Neighborhoods

Michal Kmicikiewicz, Vincent Fortuin, Ewa Szczurek

Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo

Szilvia Ujváry, Gergely Flamich, Vincent Fortuin +1

Neural Variational Gradient Descent

Lauro Langosco di Langosco, Vincent Fortuin, Heiko Strathmann

Constraining the lives and times of exoplanets through evolutionary Bayesian retrievals

Harrison Nicholls, Tim Lichtenberg, Ben Riegler +2

The paper introduces a Bayesian retrieval framework that models the time‑evolution of exoplanet interiors and atmospheres, allowing constraints on their formation histories and vol…

BNNpriors: A library for Bayesian neural network inference with different prior distributions

Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk +1

In-Context Function Learning in Large Language Models

Elif Akata, Konstantinos Voudouris, Vincent Fortuin +1

Probing as Quantifying Inductive Bias

Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin +1

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

Margherita Rosnati, Vincent Fortuin

Meta-Learning Mean Functions for Gaussian Processes

Vincent Fortuin, Heiko Strathmann, Gunnar Rätsch

Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood

Rayen Dhahri, Alexander Immer, Betrand Charpentier +2

Standard Acquisition Is Sufficient for Asynchronous Bayesian Optimization

Ben Riegler, James Odgers, Vincent Fortuin

How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization?

Agustinus Kristiadi, Felix Strieth-Kalthoff, Sriram Ganapathi Subramanian +3

Sparse MoEs meet Efficient Ensembles

James Urquhart Allingham, Florian Wenzel, Zelda E Mariet +10

On Stein Variational Neural Network Ensembles

Francesco D'Angelo, Vincent Fortuin, Florian Wenzel

Exact Langevin Dynamics with Stochastic Gradients

Adrià Garriga-Alonso, Vincent Fortuin

Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization

Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen +1

GP-VAE: Deep Probabilistic Time Series Imputation

Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch +1

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

Jonas Rothfuss, Martin Josifoski, Vincent Fortuin +1

Scalable Gaussian Process Variational Autoencoders

Metod Jazbec, Matthew Ashman, Vincent Fortuin +3

Hodge-Aware Contrastive Learning

Alexander Möllers, Alexander Immer, Vincent Fortuin +1