| 2026 | ESANN | Reliable Counterfactuals for Machine Learning Models - Current Aspects and Perspectives. | Marika Kaden, Benjamin Paassen, Barbara Hammer, Ronny Schubert, Thomas Villmann |
| 2025 | ESANN | Towards Learning Vector Quantization in the Setting of Homomorphic Encryption. | Thomas Davies, Ronny Schubert, Mandy Lange-Geisler, Klaus Dohmen, Thomas Villmann |
| 2025 | ESANN | 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 Lvdal, Elina L. van den Brandhof, Michael Biehl, Thomas Villmann |
| 2025 | ESANN | Integrating Class Relation Knowledge in Probabilistic Learning Vector Quantization. | Marika Kaden, Ronny Schubert, Tina Geweniger, Wieland Hermann, Thomas Villmann |
| 2024 | ESANN | About Vector Quantization and its Privacy in Federated Learning. | Ronny Schubert, Thomas Villmann |
| 2023 | ESANN | Variants of Neural Gas for Regression Learning. | Thomas Villmann, Ronny Schubert, Marika Kaden |
| 2022 | IJCNN | Prototype-based One-Class-Classification Learning Using Local Representations. | Daniel Staps, Ronny Schubert, Marika Kaden, Alexander Lampe, Wieland Hermann, Thomas Villmann |
| 2021 | ESANN | The LVQ-based Counter Propagation Network - an Interpretable Information Bottleneck Approach. | Marika Kaden, Ronny Schubert, Mehrdad Mohannazadeh Bakhtiari, Lucas Schwarz, Thomas Villmann |