Stefan Depeweg

Computer Science · Physical Sciences

9h-index441citations22works1.02yr avg
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Research Topics

Gaussian Processes and Bayesian Inference(5), Fault Detection and Control Systems(4), Machine Learning and Algorithms(4), Adversarial Robustness in Machine Learning(3), Neural Networks and Applications(2)

Publications22 total

Activation-Space Uncertainty Quantification for Pretrained Networks
arXiv (Cornell University)·2026Open Access
Activation-Space Uncertainty Quantification for Pretrained Networks
ArXiv.org·2026Open Access
Lifing Prediction Under Casting and Boundary Condition Deviations in Turbine Blades With 3-D Transformer-Based Neural Operators
2025
Iterative Batch Reinforcement Learning via Safe Diversified Model-based Policy Search
arXiv (Cornell University)·2024Open Access
Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning
arXiv (Cornell University)·2024· 1 citedOpen Access
Solving Bongard Problems With a Visual Language and Pragmatic Constraints
Cognitive Science·2024· 6 citedOpen Access
Uncertainty Aware Tropical Cyclone Wind Speed Estimation from Satellite Data
arXiv (Cornell University)·2024Open Access
Surrogate Models for 3D Finite Element Creep Analysis Acceleration
2023
Autonomes Lernen in Komplexen Umgebungen II
Open MIND·2019Open Access
Modeling Epistemic and Aleatoric Uncertainty with Bayesian Neural Networks and Latent Variables
mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich)·2019· 19 citedOpen Access
Solving Bongard Problems with a Visual Language and Pragmatic Reasoning
arXiv (Cornell University)·2018· 16 citedOpen Access
Sensitivity analysis for predictive uncertainty.
The European Symposium on Artificial Neural Networks·2018· 2 cited
Sensitivity Analysis for Predictive Uncertainty in Bayesian Neural Networks
arXiv (Cornell University)·2017· 6 citedOpen Access
A benchmark environment motivated by industrial control problems
2017· 47 citedOpen Access
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
arXiv (Cornell University)·2017· 120 citedOpen Access
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and\n Risk-sensitive Learning
arXiv (Cornell University)·2017· 80 citedOpen Access
Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems.
arXiv (Cornell University)·2017· 22 citedOpen Access
Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables
arXiv (Cornell University)·2017· 24 citedOpen Access
Learning and Policy Search in Stochastic Dynamical Systems with Bayesian\n Neural Networks
arXiv (Cornell University)·2016· 33 citedOpen Access
Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks
Cambridge University Engineering Department Publications Database·2016· 60 citedOpen Access
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Frequent Co-authors

Steffen Udluft(10), José Miguel Hernández-Lobato(7), Finale Doshi‐Velez(6), Thomas A. Runkler(3), Michel Tokic(2), Volkmar Sterzing(2), Frank Jäkel(2), Eric Aislan Antonelo(2), Benjamin Schrauwen(2), Eric Nalisnick(2), Nina Maria Gottschling(2), Jason Abdallah(2), Richard Bergna(2), Sergio Calvo-Ordoñez(2), Jonathan Plenk(2), Álvaro Cartea(2), Jose Miguel Hernández-Lobato(2), Daniel Hein(1), Alexander Hentschel(1), Constantin A. Rothkopf(1)