8 citations · 24 across the 28 of their papers we have counts for
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A Generalization of Amari's Bayesian Duality
Mohammad Emtiyaz Khan, Thomas Möllenhoff
Amari's contributions to information geometry and machine learning are well known. Here, we revisit Amari's work on Bayesian duality which has not received as much attention. We co…
Guiding Posterior Exploration with Optimizer-Derived Geometry
Moritz Schlager, Emanuel Sommer, Thomas Möllenhoff +1
Sampling-based methods offer a principled approach to uncertainty quantification in Bayesian neural networks. Their practical use, however, is often challenged by the computational…
SOAP-Bubbles: Structured Weight Uncertainty for Neural Networks
Adrian Robert Minut, Nico Daheim, Marco Miani +3
Structured weight-uncertainty can improve many aspects of deep learning, but it remains costly to estimate and difficult to implement. Here, we show that these issues can be addres…
Calibrated Sampling-Free Uncertainty Estimation in Bayesian Deep Learning
Tobias Jan Wieczorek, Leon de Andrade, Thomas Möllenhoff +1
Modern deep learning models remain notoriously prone to overconfidence, limiting their reliability in high-stakes applications. Bayesian methods aim to counter this by learning a d…
Joint Model and Data Sparsification via the Marginal Likelihood
Alexander Timans, Thomas Möllenhoff, Christian A. Naesseth +2
Sparse recovery in linear systems underpins applications from signal processing to high-dimensional regression. Sparse Bayesian Learning, grounded in the principle of automatic rel…
Position: agentic AI orchestration should be Bayes-consistent
Theodore Papamarkou, Pierre Alquier, Matthias Bauer +27
LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call, which expert to co…
A Stein Identity for q-Gaussians with Bounded Support
Sophia Sklaviadis, Thomas Moellenhoff, Andre F. T. Martins +2
Stein's identity is a fundamental tool in machine learning with applications in generative models, stochastic optimization, and other problems involving gradients of expectations u…