Domain-Independent Proximity Measures in Intelligent Tutoring Systems. - Bassam Mokbel; Sebastian Gross; Benjamin Paaßen; Niels Pinkwart; Barbara Hammer (EDM, 2013)

Ontology-based Extraction of Structured Information from Publications on Preclinical Experiments for Spinal Cord Injury Treatments. - Benjamin Paassen; Andreas Stöckel; Raphael Dickfelder; Jan Philip Göpfert; Nicole Brazda; Tarek Kirchhoffer; Hans Werner Müller; Roman Klinger; Matthias Hartung; Philipp Cimiano (SWAIE@COLING, 2014)

Adaptive distance measures for sequential data. - Bassam Mokbel; Benjamin Paaßen; Barbara Hammer (ESANN, 2014)

Efficient Adaptation of Structure Metrics in Prototype-Based Classification. - Bassam Mokbel; Benjamin Paaßen; Barbara Hammer (ICANN, 2014)

Example-based feedback provision using structured solution spaces. - Sebastian Gross; Bassam Mokbel; Benjamin Paaßen; Barbara Hammer; Niels Pinkwart (Int. J. Learn. Technol., 2014)

Learning interpretable kernelized prototype-based models. - Daniela Hofmann; Frank-Michael Schleif; Benjamin Paaßen; Barbara Hammer (Neurocomputing, 2014)

A Toolbox for Adaptive Sequence Dissimilarity Measures for Intelligent Tutoring Systems. - Benjamin Paaßen; Bassam Mokbel; Barbara Hammer (EDM, 2015)

Adaptive structure metrics for automated feedback provision in Java programming. - Benjamin Paassen; Bassam Mokbel; Barbara Hammer (ESANN, 2015)

Metric learning for sequences in relational LVQ. - Bassam Mokbel; Benjamin Paaßen; Frank-Michael Schleif; Barbara Hammer (Neurocomputing, 2015)

Interview with Werner von Seelen. - Benjamin Paaßen (Künstliche Intell., 2015)

Autonomous Learning of Representations. - Oliver Walter; Reinhold Haeb-Umbach; Bassam Mokbel; Benjamin Paaßen; Barbara Hammer (Künstliche Intell., 2015)

Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming. - Benjamin Paaßen; Joris Jensen; Barbara Hammer (EDM, 2016)

Gaussian process prediction for time series of structured data. - Benjamin Paassen; Christina Göpfert; Barbara Hammer (ESANN, 2016)

Convergence of Multi-pass Large Margin Nearest Neighbor Metric Learning. - Christina Göpfert; Benjamin Paassen; Barbara Hammer (ICANN (1), 2016)

Local Reject Option for Deterministic Multi-class SVM. - Johannes Kummert; Benjamin Paassen; Joris Jensen; Christina Göpfert; Barbara Hammer (ICANN (2), 2016)

Adaptive structure metrics for automated feedback provision in intelligent tutoring systems. - Benjamin Paaßen; Bassam Mokbel; Barbara Hammer (Neurocomputing, 2016)

Echo State Networks as Novel Approach for Low-Cost Myoelectric Control. - Cosima Prahm; Alexander Schulz 001; Benjamin Paaßen; Oskar Aszmann; Barbara Hammer; Georg Dorffner (AIME, 2017)

An EM transfer learning algorithm with applications in bionic hand prostheses. - Benjamin Paassen; Alexander Schulz 001; Janne Hahne; Barbara Hammer (ESANN, 2017)

Linear supervised transfer learning for the large margin nearest neighbor classifier. - Kolja Berger; Alexander Schulz 001; Benjamin Paaßen; Barbara Hammer (SSCI, 2017)

The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces. - Benjamin Paaßen; Barbara Hammer; Thomas William Price; Tiffany Barnes; Sebastian Gross; Niels Pinkwart (CoRR, 2017)

Tree Edit Distance Learning via Adaptive Symbol Embeddings. - Benjamin Paaßen; Claudio Gallicchio; Alessio Micheli; Barbara Hammer (ICML, 2018)

Revisiting the tree edit distance and its backtracing: A tutorial. - Benjamin Paaßen (CoRR, 2018)

Tree Edit Distance Learning via Adaptive Symbol Embeddings: Supplementary Materials and Results. - Benjamin Paaßen (CoRR, 2018)

Expectation maximization transfer learning and its application for bionic hand prostheses. - Benjamin Paaßen; Alexander Schulz 001; Janne Hahne; Barbara Hammer (Neurocomputing, 2018)

Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces. - Benjamin Paaßen; Christina Göpfert; Barbara Hammer (Neural Process. Lett., 2018)

Dynamic fairness - Breaking vicious cycles in automatic decision making. - Benjamin Paaßen; Astrid Bunge; Carolin Hainke; Leon Sindelar; Matthias Vogelsang (ESANN, 2019)

Embeddings and Representation Learning for Structured Data. - Benjamin Paaßen; Claudio Gallicchio; Alessio Micheli; Alessandro Sperduti (ESANN, 2019)

Adversarial Edit Attacks for Tree Data. - Benjamin Paaßen (IDEAL (1), 2019)

A Comparison of the Quality of Data-Driven Programming Hint Generation Algorithms. - Thomas W. Price; Yihuan Dong; Rui Zhi; Benjamin Paaßen; Nicholas Lytle; Veronica Cateté; Tiffany Barnes (Int. J. Artif. Intell. Educ., 2019)

Metric Learning for Structured Data. - Benjamin Paaßen (2019)

Reservoir memory machines. - Benjamin Paassen; Alexander Schulz 001 (ESANN, 2020)

Tree Echo State Autoencoders with Grammars. - Benjamin Paassen; Irena Koprinska; Kalina Yacef (IJCNN, 2020)

The Gendered Nature and Malleability of Gamer Stereotypes. - Thekla Morgenroth; Michelle Stratemeyer; Benjamin Paaßen (Cyberpsychology Behav. Soc. Netw., 2020)

Progress Networks as a Tool for Analysing Student Programming Difficulties. - Jessica McBroom; Benjamin Paassen; Bryn Jeffries; Irena Koprinska; Kalina Yacef (ACE, 2021)

Modeling Creativity in Visual Programming: From Theory to Practice. - Anastasia Kovalkov; Benjamin Paassen; Avi Segal; Kobi Gal; Niels Pinkwart (EDM, 2021)

Analyzing Student Success and Mistakes in Virtual Microscope Structure Search Tasks. - Benjamin Paaßen; Andreas Bertsch; Katharina Langer-Fischer; Sylvio Rüdian; Xia Wang 003; Rupali Sinha; Jakub Kuzilek; Stefan Britsch; Niels Pinkwart (EDM, 2021)

ast2vec: Utilizing Recursive Neural Encodings of Python Programs. - Benjamin Paaßen; Jessica McBroom; Bryn Jeffries; Irena Koprinska; Kalina Yacef (EDM, 2021)

Next Steps for Next-step Hints: Lessons Learned from Teacher Evaluations of Automatic Programming Hints (Full Paper). - Benjamin Paaßen; Jessica McBroom; Bryn Jeffries; Irena Koprinska; Kalina Yacef (EDM (Workshops), 2021)

Deep learning for graphs. - Davide Bacciu; Filippo Maria Bianchi; Benjamin Paassen; Cesare Alippi (ESANN, 2021)

Graph Edit Networks. - Benjamin Paassen; Daniele Grattarola; Daniele Zambon; Cesare Alippi; Barbara Hammer (ICLR, 2021)

An A*-algorithm for the Unordered Tree Edit Distance with Custom Costs. - Benjamin Paaßen (SISAP, 2021)

Automatic Creativity Measurement in Scratch Programs Across Modalities. - Anastasia Kovalkov; Benjamin Paaßen; Avi Segal; Niels Pinkwart; Kobi Gal (IEEE Trans. Learn. Technol., 2021)

Interpretable Knowledge Gain Prediction for Vocational Preparatory E-Learnings. - Benjamin Paaßen; Malwina Dywel; Melanie Fleckenstein; Niels Pinkwart (AIED (2), 2022)

Few-shot Keypose Detection for Learning of Psychomotor Skills. - Benjamin Paaßen; Tobias Baumgartner 002; Mai Geisen; Nina Riedl; Milos Kravcik (MILeS@EC-TEL, 2022)

Sparse Factor Autoencoders for Item Response Theory. - Benjamin Paaßen; Malwina Dywel; Melanie Fleckenstein; Niels Pinkwart (EDM, 2022)

Faster Confidence Intervals for Item Response Theory via an Approximate Likelihood Profile. - Benjamin Paaßen; Christina Göpfert; Niels Pinkwart (EDM, 2022)

Combining domain modelling and student modelling techniques in a single automated pipeline. - Gio Picones; Benjamin Paaßen; Irena Koprinska; Kalina Yacef (EDM, 2022)

Reservoir stack machines. - Benjamin Paaßen; Alexander Schulz 001; Barbara Hammer (Neurocomputing, 2022)

Recursive tree grammar autoencoders. - Benjamin Paaßen; Irena Koprinska; Kalina Yacef (Mach. Learn., 2022)

Reservoir Memory Machines as Neural Computers. - Benjamin Paaßen; Alexander Schulz 001; Terrence C. Stewart; Barbara Hammer (IEEE Trans. Neural Networks Learn. Syst., 2022)

Was wirkt? Eine Literaturstudie zur Wirksamkeit von Systemeigenschaften in Mathematik-Lernumgebungen. - Berit Blanc; Insa Reichow; Benjamin Paaßen (DELFI, 2023)

Diffusion-Based Visual Counterfactual Explanations - Towards Systematic Quantitative Evaluation. - Philipp Väth; Alexander M. Frühwald; Benjamin Paassen; Magda Gregorova (PKDD/ECML Workshops (3), 2023)

Fairness in KI-Systemen. - Janine Strotherm; Alissa Müller; Barbara Hammer; Benjamin Paaßen (CoRR, 2023)

Evaluating Task-Level Struggle Detection Methods in Intelligent Tutoring Systems for Programming. - Jesper Dannath; Alina Deriyeva; Benjamin Paaßen (DeLFI, 2024)

Relation between struggle and learning personality in programming exercises. - Alina Deriyeva; Jesper Dannath; Benjamin Paaßen (DeLFI, 2024)

Case study: Using LLMs to assist with solving programming homework assignments. - Alina Deriyeva; Jesper Dannath; Benjamin Paaßen (DELFI Workshops, 2024)

Leveraging Local Data Sampling Strategies to Improve Federated Learning (Extended Abstract). - Christoph Düsing; Philipp Cimiano; Benjamin Paaßen (DSAA, 2024)

Automatic Matchmaking in Two-Versus-Two Sports. - Sören Rüttgers; Ulrike Kuhl; Benjamin Paaßen (EDM, 2024)

Fine-Grained Detection of Solidarity for Women and Migrants in 155 Years of German Parliamentary Debates. - Aida Kostikova; Dominik Beese; Benjamin Paassen; Ole Pütz; Gregor Wiedemann; Steffen Eger (EMNLP, 2024)

Few-shot similarity learning for motion classification via electromyography. - Rui Liu; Benjamin Paassen (ESANN, 2024)

Tumor Grading via Decorrelated Sparse Survival Regression. - Benjamin Paassen; Nadine T. Gaisa; Michael Rose; Mark-Sebastian Bösherz (ESANN, 2024)

EdTec-QBuilder: A Semantic Retrieval Tool for Assembling Vocational Training Exams in German Language. - Alonso Palomino; Andreas Fischer; Jakub Kuzilek; Jarek Nitsch; Niels Pinkwart; Benjamin Paassen (NAACL (Demonstrations), 2024)

K Minimum Enclosing Balls for Outlier Detection. - Daniel Staps; Thomas Villmann; Benjamin Paaßen (WSOM+, 2024)

FairGLVQ: Fairness in Partition-Based Classification. - Felix Störck; Fabian Hinder; Johannes Brinkrolf; Benjamin Paassen; Valerie Vaquet; Barbara Hammer (WSOM+, 2024)

Continual Imitation Learning for Prosthetic Limbs. - Sharmita Dey; Benjamin Paassen; Sarath R. Nair; Sabri Boughorbel; Arndt F. Schilling (CoRR, 2024)

GradCheck: Analyzing classifier guidance gradients for conditional diffusion sampling. - Philipp Väth; Alexander M. Frühwald; Benjamin Paassen; Magda Gregorova (CoRR, 2024)

Generative Example-Based Explanations: Bridging the Gap between Generative Modeling and Explainability. - Philipp Väth; Alexander M. Frühwald; Benjamin Paassen; Magda Gregorova (CoRR, 2024)

Aligning Generalisation Between Humans and Machines. - Filip Ilievski; Barbara Hammer; Frank van Harmelen; Benjamin Paassen; Sascha Saralajew; Ute Schmid; Michael Biehl; Marianna Bolognesi; Xin Luna Dong; Kiril Gashteovski; Pascal Hitzler; Giuseppe Marra; et al. (CoRR, 2024)

What is a Step? A User Study on How to Sub-divide the Solution Process of Introductory Python Tasks. - Jesper Dannath; Alina Deriyeva; Benjamin Paassen (EDM, 2025)

Mitigating Bias in Item Retrieval for Enhancing Exam Assembly in Vocational Education Services. - Alonso Palomino; Andreas Fischer; David Buschhüter; Roland Roller; Niels Pinkwart; Benjamin Paassen (NAACL (Industry Track), 2025)

Continual Learning from Simulated Interactions via Multitask Prospective Rehearsal for Bionic Limb Behavior Modeling. - Sharmita Dey; Benjamin Paassen; Sarath Ravindran Nair; Sabri Boughorbel; Arndt F. Schilling (Trans. Mach. Learn. Res., 2025)

Healthy Distrust in AI systems. - Benjamin Paaßen; Suzana Alpsancar; Tobias Matzner; Ingrid Scharlau (CoRR, 2025)

LLLMs: A Data-Driven Survey of Evolving Research on Limitations of Large Language Models. - Aida Kostikova; Zhipin Wang; Deidamea Bajri; Ole Pütz; Benjamin Paaßen; Steffen Eger (CoRR, 2025)

Diffusion Classifier Guidance for Non-robust Classifiers. - Philipp Väth; Dibyanshu Kumar; Benjamin Paassen; Magda Gregorová (CoRR, 2025)

Large Language Models Do Not Simulate Human Psychology. - Sarah Schröder; Thekla Morgenroth; Ulrike Kuhl; Valerie Vaquet; Benjamin Paaßen (CoRR, 2025)

Assessing LLM Text Detection in Educational Contexts: Does Human Contribution Affect Detection? - Lukas Gehring; Benjamin Paaßen (CoRR, 2025)

Leveraging local data sampling strategies to improve federated learning. - Christoph Düsing; Philipp Cimiano; Benjamin Paaßen (Int. J. Data Sci. Anal., 2025)