| 2026 | ESANN | Evaluation of Rashomon Sets for the Determination of Stable and Plausible Model Explanations. | Marika Kaden, Mahrokh Karimi, Subhashree Panda, Thomas Pfaff, Thomas Villmann |
| 2026 | ESANN | Reliable Counterfactuals for Machine Learning Models - Current Aspects and Perspectives. | Marika Kaden, Benjamin Paassen, Barbara Hammer, Ronny Schubert, Thomas Villmann |
| 2026 | ESANN | Geometric-analytical Generation of Counterfactuals for Prototype-based Classifiers. | Marika Kaden, Lynn V. Reuss, Thomas Villmann |
| 2026 | ESANN | Enforcing Feature Sparseness for Reliable Classification by Prototype-Based Models. | Marika Kaden, Julius Voigt, Sascha Saralajew, 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 |
| 2025 | ICAISC | Reliable Classification Learning for Medical Data Analysis Using Prototype-Based Models. | Julius Voigt, Marika Kaden, Lynn V. Reuss, Thomas Villmann |
| 2024 | ESANN | Domain Knowledge Integration in Machine Learning Systems - An Introduction. | Marika Kaden, Sascha Saralajew, Thomas Villmann |
| 2023 | ESANN | Variants of Neural Gas for Regression Learning. | Thomas Villmann, Ronny Schubert, Marika Kaden |
| 2022 | ESANN | Efficient classification learning of biochemical structured data by means of relevance weighting for sensoric response features. | Katrin Sophie Bohnsack, Marika Kaden, Julius Voigt, Thomas Villmann |
| 2022 | ICONIP | Trustworthiness and Confidence of Gait Phase Predictions in Changing Environments Using Interpretable Classifier Models. | Danny Mbius, Jensun Ravichandran, Marika Kaden, Thomas Villmann |
| 2022 | IJCNN | Prototype-based One-Class-Classification Learning Using Local Representations. | Daniel Staps, Ronny Schubert, Marika Kaden, Alexander Lampe, Wieland Hermann, Thomas Villmann |
| 2022 | IDA | A Learning Vector Quantization Architecture for Transfer Learning Based Classification in Case of Multiple Sources by Means of Null-Space Evaluation. | Thomas Villmann, Daniel Staps, Jensun Ravichandran, Sascha Saralajew, Michael Biehl, Marika Kaden |
| 2021 | ESANN | The LVQ-based Counter Propagation Network - an Interpretable Information Bottleneck Approach. | Marika Kaden, Ronny Schubert, Mehrdad Mohannazadeh Bakhtiari, Lucas Schwarz, Thomas Villmann |
| 2021 | ESANN | RecLVQ: Recurrent Learning Vector Quantization. | Jensun Ravichandran, Thomas Villmann, Marika Kaden |
| 2021 | ICAISC | Possibilistic Classification Learning Based on Contrastive Loss in Learning Vector Quantizer Networks. | Seyedfakhredin Musavishavazi, Marika Kaden, Thomas Villmann |
| 2020 | ESANN | Quantum-Inspired Learning Vector Quantization for Classification Learning. | Thomas Villmann, Jensun Ravichandran, Alexander Engelsberger, Andrea Villmann, Marika Kaden |
| 2019 | ICAISC | Appropriate Data Density Models in Probabilistic Machine Learning Approaches for Data Analysis. | Thomas Villmann, Marika Kaden, Mehrdad Mohannazadeh Bakhtiari, Andrea Villmann |
| 2018 | ESANN | Reliable Patient Classification in Case of Uncertain Class Labels Using a Cross-Entropy Approach. | Andrea Villmann, Marika Kaden, Sascha Saralajew, Wieland Hermann, Thomas Villmann |
| 2018 | ICAISC | Probabilistic Learning Vector Quantization with Cross-Entropy for Probabilistic Class Assignments in Classification Learning. | Andrea Villmann, Marika Kaden, Sascha Saralajew, Thomas Villmann |
| 2016 | ESANN | Adaptive dissimilarity weighting for prototype-based classification optimizing mixtures of dissimilarities. | Marika Kaden, David Nebel, Thomas Villmann |
| 2016 | ICAISC | Similarities, Dissimilarities and Types of Inner Products for Data Analysis in the Context of Machine Learning - A Mathematical Characterization. | Thomas Villmann, Marika Kaden, David Nebel, Andrea Bohnsack |
| 2015 | CAIP | Learning Vector Quantization with Adaptive Cost-Based Outlier-Rejection. | Thomas Villmann, Marika Kaden, David Nebel, Michael Biehl |
| 2015 | ESANN | Learning matrix quantization and variants of relevance learning. | Kristin Domaschke, Marika Kaden, Mandy Lange, Thomas Villmann |
| 2015 | ICAISC | Mathematical Characterization of Sophisticated Variants for Relevance Learning in Learning Matrix Quantization Based on Schatten-p-norms. | Andrea Bohnsack, Kristin Domaschke, Marika Kaden, Mandy Lange, Thomas Villmann |
| 2014 | CIDM | Precision-Recall-Optimization in Learning Vector Quantization Classifiers for Improved Medical Classification Systems. | Thomas Villmann, Marika Kaden, Mandy Lange, Paul Sturmer, Wieland Hermann |
| 2014 | ESANN | Optimization of General Statistical Accuracy Measures for Classification Based on Learning Vector Quantization. | Marika Kaden, Wieland Hermann, Thomas Villmann |