113 citations · 208 across the 11 of their papers we have counts for
12 papers
Decoupled PFNs: Identifiable Epistemic-Aleatoric Decomposition via Structured Synthetic Priors
Richard Bergna, Stefan Depeweg, José Miguel Hernández-Lobato
Prior-Fitted Networks (PFNs) amortize Bayesian prediction by meta-learning over a synthetic task prior, but their standard output is a posterior predictive distribution over noisy…
Activation-Space Uncertainty Quantification for Pretrained Networks
Richard Bergna, Stefan Depeweg, Sergio Calvo-Ordoñez +3
Reliable uncertainty estimates are crucial for deploying pretrained models; yet, many strong methods for quantifying uncertainty require retraining, Monte Carlo sampling, or expens…
Post-Hoc Uncertainty Quantification in Pre-Trained Neural Networks via Activation-Level Gaussian Processes
Richard Bergna, Stefan Depeweg, Sergio Calvo Ordonez +3
Uncertainty quantification in neural networks through methods such as Dropout, Bayesian neural networks and Laplace approximations is either prone to underfitting or computationall…
Iterative Batch Reinforcement Learning via Safe Diversified Model-based Policy Search
Amna Najib, Stefan Depeweg, Phillip Swazinna
Batch reinforcement learning enables policy learning without direct interaction with the environment during training, relying exclusively on previously collected sets of interactio…
Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning
Nils Lehmann, Jakob Gawlikowski, Adam J. Stewart +4
Uncertainty quantification (UQ) is an essential tool for applying deep neural networks (DNNs) to real world tasks, as it attaches a degree of confidence to DNN outputs. However, de…
Uncertainty Aware Tropical Cyclone Wind Speed Estimation from Satellite Data
Nils Lehmann, Nina Maria Gottschling, Stefan Depeweg +1
Deep neural networks (DNNs) have been successfully applied to earth observation (EO) data and opened new research avenues. Despite the theoretical and practical advances of these t…
Solving Bongard Problems with a Visual Language and Pragmatic Reasoning
Stefan Depeweg, Constantin A. Rothkopf, Frank Jäkel
More than 50 years ago Bongard introduced 100 visual concept learning problems as a testbed for intelligent vision systems. These problems are now known as Bongard problems. Althou…
Sensitivity Analysis for Predictive Uncertainty in Bayesian Neural Networks
Stefan Depeweg, José Miguel Hernández-Lobato, Steffen Udluft +1
We derive a novel sensitivity analysis of input variables for predictive epistemic and aleatoric uncertainty. We use Bayesian neural networks with latent variables as a model class…
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez +1
Bayesian neural networks with latent variables are scalable and flexible probabilistic models: They account for uncertainty in the estimation of the network weights and, by making…
A Benchmark Environment Motivated by Industrial Control Problems
Daniel Hein, Stefan Depeweg, Michel Tokic +4
In the research area of reinforcement learning (RL), frequently novel and promising methods are developed and introduced to the RL community. However, although many researchers are…
Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables
Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez +1
Bayesian neural networks (BNNs) with latent variables are probabilistic models which can automatically identify complex stochastic patterns in the data. We describe and study in th…
Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks
Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez +1
We present an algorithm for model-based reinforcement learning that combines Bayesian neural networks (BNNs) with random roll-outs and stochastic optimization for policy learning.…