Transfer learning for the probabilistic classification vector machine. - Christoph Raab; Frank-Michael Schleif (COPA, 2018)

Sparse Transfer Classification for Text Documents. - Christoph Raab; Frank-Michael Schleif (KI, 2018)

Sparsification of Indefinite Learning Models. - Frank-Michael Schleif; Christoph Raab; Peter Tiño (S+SSPR, 2018)

Reactive Soft Prototype Computing for frequent reoccurring Concept Drift. - Christoph Raab; Moritz Heusinger; Frank-Michael Schleif (ESANN, 2019)

Passive Concept Drift Handling via Momentum Based Robust Soft Learning Vector Quantization. - Moritz Heusinger; Christoph Raab; Frank-Michael Schleif (WSOM+, 2019)

Domain Adaptation via Low-Rank Basis Approximation. - Christoph Raab; Frank-Michael Schleif (CoRR, 2019)

Bridging Adversarial and Statistical Domain Transfer via Spectral Adaptation Networks. - Christoph Raab; Philipp Väth; Peter Meier 005; Frank-Michael Schleif (ACCV (3), 2020)

Analyzing Dynamic Social Media Data via Random Projection - A New Challenge for Stream Classifiers. - Moritz Heusinger; Christoph Raab; Frank-Michael Schleif (EAIS, 2020)

Domain Invariant Representations with Deep Spectral Alignment. - Christoph Raab; Peter Meier 005; Frank-Michael Schleif (ESANN, 2020)

Structure Preserving Encoding of Non-euclidean Similarity Data. - Maximilian Münch; Christoph Raab; Michael Biehl; Frank-Michael Schleif (ICPRAM, 2020)

Encoding of Indefinite Proximity Data: A Structure Preserving Perspective. - Maximilian Münch; Christoph Raab; Frank-Michael Schleif (ICPRAM (Revised Selected Papers), 2020)

Low-Rank Subspace Override for Unsupervised Domain Adaptation. - Christoph Raab; Frank-Michael Schleif (KI, 2020)

Data-Driven Supervised Learning for Life Science Data. - Maximilian Münch; Christoph Raab; Michael Biehl; Frank-Michael Schleif (Frontiers Appl. Math. Stat., 2020)

Reactive Soft Prototype Computing for Concept Drift Streams. - Christoph Raab; Moritz Heusinger; Frank-Michael Schleif (Neurocomputing, 2020)

Transfer learning extensions for the probabilistic classification vector machine. - Christoph Raab; Frank-Michael Schleif (Neurocomputing, 2020)

Sparsification of core set models in non-metric supervised learning. - Frank-Michael Schleif; Christoph Raab; Peter Tiño (Pattern Recognit. Lett., 2020)

Federated Learning - Methods, Applications and beyond. - Moritz Heusinger; Christoph Raab; Fabrice Rossi; Frank-Michael Schleif (ESANN, 2021)

Domain Adversarial Tangent Learning Towards Interpretable Domain Adaptation. - Christoph Raab; Sascha Saralajew; Frank-Michael Schleif (ESANN, 2021)

Adaptive multi-modal positive semi-definite and indefinite kernel fusion for binary classification. - Maximilian Münch; Christoph Raab; Simon Heilig; Manuel Röder; Frank-Michael Schleif (ESANN, 2022)

Dimensionality reduction in the context of dynamic social media data streams. - Moritz Heusinger; Christoph Raab; Frank-Michael Schleif (Evol. Syst., 2022)

Domain adversarial tangent subspace alignment for explainable domain adaptation. - Christoph Raab; Manuel Röder; Frank-Michael Schleif (Neurocomputing, 2022)

Passive concept drift handling via variations of learning vector quantization. - Moritz Heusinger; Christoph Raab; Frank-Michael Schleif (Neural Comput. Appl., 2022)

Learning in non-stationary Environments. - Christoph Raab (2022)

Static and adaptive subspace information fusion for indefinite heterogeneous proximity data. - Maximilian Münch; Manuel Röder; Simon Heilig; Christoph Raab; Frank-Michael Schleif (Neurocomputing, 2023)

Crossing Domain Borders with Federated Few-Shot Adaptation. - Manuel Röder; Maximilian Münch; Christoph Raab; Frank-Michael Schleif (ICPRAM, 2024)

Domain Borders Are There to Be Crossed With Federated Few-Shot Adaptation. - Manuel Röder; Christoph Raab; Frank-Michael Schleif (CoRR, 2025)