| 2026 | ESANN | Diminishing Returns - Data Integer Quantization and its Effects on Training Dynamics of Distance Based Classifiers. | Thomas Davies, Alexander Engelsberger, Magdalena Psenickova, 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 | ICAISC | Prototype Learning for Classification on Spherical Manifolds and Its Relation to Quantum Classification Approaches. | Alexander Engelsberger, Magdalena Psenickova, Thomas Villmann |
| 2023 | ESANN | Quantum-ready vector quantization: Prototype learning as a binary optimization problem. | Alexander Engelsberger, Thomas Villmann |
| 2023 | ESANN | Sparse Nystrm Approximation for Non-Vectorial Data Using Class-informed Landmark Selection. | Maximilian Mnch, Katrin Sophie Bohnsack, Alexander Engelsberger, Frank-Michael Schleif, Thomas Villmann |
| 2022 | ICAISC | Multilayer Perceptrons with Banach-Like Perceptrons Based on Semi-inner Products - About Approximation Completeness. | Thomas Villmann, Alexander Engelsberger |
| 2021 | ICAISC | Quantum-Hybrid Neural Vector Quantization - A Mathematical Approach. | Thomas Villmann, Alexander Engelsberger |
| 2020 | ESANN | Quantum-Inspired Learning Vector Quantization for Classification Learning. | Thomas Villmann, Jensun Ravichandran, Alexander Engelsberger, Andrea Villmann, Marika Kaden |