Stefan T. Radev

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 25

Top Topics

Stochastic Models

Bayesian Inference

Top Venues

IEEE Trans. Neural Networks Learn. Syst.

Publications

Marvin Schmitt, Yuga Hikida, Stefan T. Radev, Filip Sadlo, Paul-Christian Bürkner
The Simplex Projection: Lossless Visualization of 4D Compositional Data on a 2D Canvas. CoRR (2024)
Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation. CoRR (2024)
Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner
JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models. CoRR (2023)
Jens Müller, Lars Kühmichel, Martin Rohbeck, Stefan T. Radev, Ullrich Köthe
Towards Context-Aware Domain Generalization: Representing Environments with Permutation-Invariant Networks. CoRR (2023)
Marvin Schmitt, Stefan T. Radev, Paul-Christian Bürkner
Fuse It or Lose It: Deep Fusion for Multimodal Simulation-Based Inference. CoRR (2023)
Jens Müller, Stefan T. Radev, Robert Schmier, Felix Draxler, Carsten Rother, Ullrich Köthe
Finding Competence Regions in Domain Generalization. CoRR (2023)
Lasse Elsemüller, Hans Olischläger, Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev
Sensitivity-Aware Amortized Bayesian Inference. CoRR (2023)
Marvin Schmitt, Stefan T. Radev, Paul-Christian Bürkner
Meta-Uncertainty in Bayesian Model Comparison. AISTATS (2023)
Stefan T. Radev, Marvin Schmitt, Lukas Schumacher, Lasse Elsemüller, Valentin Pratz, Yannik Schälte, Ullrich Köthe, Paul-Christian Bürkner
BayesFlow: Amortized Bayesian Workflows With Neural Networks. J. Open Source Softw. 8 (90) (2023)
Stefan T. Radev, Marco D'Alessandro, Ulf K. Mertens, Andreas Voss, Ullrich Köthe, Paul-Christian Bürkner
Amortized Bayesian Model Comparison With Evidential Deep Learning. IEEE Trans. Neural Networks Learn. Syst. 34 (8) (2023)
Marvin Schmitt, Valentin Pratz, Ullrich Köthe, Paul-Christian Bürkner, Stefan T. Radev
Consistency Models for Scalable and Fast Simulation-Based Inference. CoRR (2023)
Lasse Elsemüller, Martin Schnuerch, Paul-Christian Bürkner, Stefan T. Radev
A Deep Learning Method for Comparing Bayesian Hierarchical Models. CoRR (2023)
Stefan T. Radev, Marvin Schmitt, Lukas Schumacher, Lasse Elsemüller, Valentin Pratz, Yannik Schälte, Ullrich Köthe, Paul-Christian Bürkner
BayesFlow: Amortized Bayesian Workflows With Neural Networks. CoRR (2023)
Jens Müller, Stefan T. Radev, Robert Schmier, Felix Draxler, Carsten Rother, Ullrich Köthe
Finding Competence Regions in Domain Generalization. Trans. Mach. Learn. Res. 2023 (2023)
Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks. DAGM (2023)
Marvin Schmitt, Daniel Habermann, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev
Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference. CoRR (2023)
Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner
Jana: Jointly amortized neural approximation of complex Bayesian models. UAI (2023)
Marvin Schmitt, Stefan T. Radev, Paul-Christian Bürkner
Meta-Uncertainty in Bayesian Model Comparison. CoRR (2022)
Stefan T. Radev, Ulf K. Mertens, Andreas Voss, Lynton Ardizzone, Ullrich Köthe
BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 33 (4) (2022)
Stefan T. Radev, Frederik Graw, Simiao Chen, Nico T. Mutters, Vanessa Eichel, Till Bärnighausen, Ullrich Köthe
OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany. PLoS Comput. Biol. 17 (10) (2021)
Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev
BayesFlow can reliably detect Model Misspecification and Posterior Errors in Amortized Bayesian Inference. CoRR (2021)
Stefan T. Radev, Frederik Graw, Simiao Chen, Nico T. Mutters, Vanessa Eichel, Till Bärnighausen, Ullrich Köthe
Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks. CoRR (2020)
Stefan T. Radev, Ulf K. Mertens, Andreas Voss, Lynton Ardizzone, Ullrich Köthe
BayesFlow: Learning complex stochastic models with invertible neural networks. CoRR (2020)
Stefan T. Radev, Andreas Voss, Eva Marie Wieschen, Paul-Christian Bürkner
Amortized Bayesian Inference for Models of Cognition. CoRR (2020)
Stefan T. Radev, Marco D'Alessandro, Paul-Christian Bürkner, Ulf K. Mertens, Andreas Voss, Ullrich Köthe
Amortized Bayesian model comparison with evidential deep learning. CoRR (2020)