Ronny Schubert

According to our database, Ronny Schubert authored at least 12 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Privacy-preserving nearest prototype classifier.

Ronny Schubert, Thomas Davies, Mandy Lange-Geisler, Klaus Dohmen, Thomas Villmann

Neurocomputing, 2026

FA(IR)2MA-GLVQ - A hidden-feature-bias mitigation approach for fairness in classification learning based on generalized matrix learning vector quantization.

Marika Kaden, Ronny Schubert, Julius Voigt, Lynn V. Reuss, Alexander Engelsberger, Sofie Lövdal, Elina L. van den Brandhof, Michael Biehl, Thomas Villmann

Neurocomputing, 2026

Reliable Counterfactuals for Machine Learning Models - Current Aspects and Perspectives.

Marika Kaden, Benjamin Paassen, Barbara Hammer, Ronny Schubert, Thomas Villmann

Proceedings of the 34th European Symposium on Artificial Neural Networks, 2026

2025

Integrating Class Relation Knowledge in Probabilistic Learning Vector Quantization.

Marika Kaden, Ronny Schubert, Tina Geweniger, Wieland Hermann, Thomas Villmann

Proceedings of the 33rd European Symposium on Artificial Neural Networks, 2025

Mitigating the Bias in Data for Fairness Using an Advanced Generalized Learning Vector Quantization Approach - FA(IR)$^2$MA-GLVQ.

Marika Kaden, Alexander Engelsberger, Ronny Schubert, Sofie Lövdal, Elina L. van den Brandhof, Michael Biehl, Thomas Villmann

Proceedings of the 33rd European Symposium on Artificial Neural Networks, 2025

Towards Learning Vector Quantization in the Setting of Homomorphic Encryption.

Thomas Davies, Ronny Schubert, Mandy Lange-Geisler, Klaus Dohmen, Thomas Villmann

Proceedings of the 33rd European Symposium on Artificial Neural Networks, 2025

2024

About Interpretable Learning Rules for Vector Quantizers - A Methodological Approach.

Ronny Schubert, Thomas Villmann

Proceedings of the Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and Beyond, 2024

About Vector Quantization and its Privacy in Federated Learning.

Ronny Schubert, Thomas Villmann

Proceedings of the 32nd European Symposium on Artificial Neural Networks, 2024

2023

A White-Box Workflow for the Prediction of Food Content From Near-Infrared Data Based on Fourier-Transformation.

Ronny Schubert, Lynn V. Reuss, Daniel Staps, Marika Kaden, Thomas Villmann, Robert Hasler, Robin Herz, Till Tiemann, Wolfram Richardt

Proceedings of the 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, 2023

Variants of Neural Gas for Regression Learning.

Thomas Villmann, Ronny Schubert, Marika Kaden

Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

2022

Prototype-based One-Class-Classification Learning Using Local Representations.

Daniel Staps, Ronny Schubert, Marika Kaden, Alexander Lampe, Wieland Hermann, Thomas Villmann

Proceedings of the International Joint Conference on Neural Networks, 2022

2021

The LVQ-based Counter Propagation Network - an Interpretable Information Bottleneck Approach.

Marika Kaden, Ronny Schubert, Mehrdad Mohannazadeh Bakhtiari, Lucas Schwarz, Thomas Villmann

Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021