| 2026 | AAAI | Can Humans Teach Machines to Code? | Cline Hocquette, Johannes Langer, Andrew Cropper, Ute Schmid |
| 2026 | KI | Multi-state PatchCore for Noisy Industrial Audio. | Simin Mirzadeh, Shamim Miroliaei, Alexej Zitzer, Johannes Munk, Matthias W. Gempel, Ute Schmid |
| 2026 | KI | Explainable-AI-Based Training for Relevance-Based Robust Reinforcement Learning. | Benedikt Schlereth-Groh, Sakir Furkan Yndem, Ramin Tavakoli Kolagari, Ute Schmid |
| 2025 | KI | Toward Simple and Robust Contrastive Explanations for Image Classification by Leveraging Instance Similarity and Concept Relevance. | Yuliia Kaidashova, Bettina Finzel, Ute Schmid |
| 2025 | KI | Towards Observing the Effect of Abstraction on Understandability of Explanations in Answer Set Programming. | Zeynep G. Saribatur, Johannes Langer, Anna Magdalena Thaler, Ute Schmid |
| 2025 | KI | XAIRob - An Explainable-AI-Based Relative Robustness Measure for Object Detection. | Benedikt Schlereth-Groh, Ramin Tavakoli Kolagari, Ute Schmid |
| 2025 | KI | Re-evaluating the Robustness and Interpretability of the Contrastive Explanations Method for Image Classification. | Luisa Schneider, Bettina Finzel, Ute Schmid |
| 2024 | ECCV | Interactive Explainable Anomaly Detection for Industrial Settings. | Daniel Gramelt, Timon Hfer, Ute Schmid |
| 2024 | IROS | FruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework. | Lukas Meyer, Andreas Gilson, Ute Schmid, Marc Stamminger |
| 2024 | IDEAL | Near Hit and Near Miss Example Explanations for Model Revision in Binary Image Classification. | Bettina Finzel, Judith Knoblach, Anna Magdalena Thaler, Ute Schmid |
| 2024 | KI | Explanatory Interactive Machine Learning with Counterexamples from Constrained Large Language Models. | Emanuel Slany, Stephan Scheele, Ute Schmid |
| 2023 | ECAI | Bayesian CAIPI: A Probabilistic Approach to Explanatory and Interactive Machine Learning. | Emanuel Slany, Stephan Scheele, Ute Schmid |
| 2023 | KSEM | Cluster Robust Inference for Embedding-Based Knowledge Graph Completion. | Simon Schramm, Ulrich Niklas, Ute Schmid |
| 2022 | AAAI | An Interactive Explanatory AI System for Industrial Quality Control. | Dennis Mller, Michael Mrz, Stephan Scheele, Ute Schmid |
| 2022 | ICPR | CorrLoss: Integrating Co-Occurrence Domain Knowledge for Affect Recognition. | Ines Rieger, Jaspar Pahl, Bettina Finzel, Ute Schmid |
| 2022 | ILP | Explaining with Attribute-Based and Relational Near Misses: An Interpretable Approach to Distinguishing Facial Expressions of Pain and Disgust. | Bettina Finzel, Simon P. Kuhn, David E. Tafler, Ute Schmid |
| 2022 | KI | Explaining Hate Speech Classification with Model-Agnostic Methods. | Durgesh Nandini, Ute Schmid |
| 2021 | CogSci | Explaining Machine Learned Relational Concepts in Visual Domains - Effects of Perceived Accuracy on Joint Performance and Trust. | Anna Magdalena Thaler, Ute Schmid |
| 2021 | ETFA | Anomaly Detection for Hydraulic Systems under Test. | Deniz Neufeld, Ute Schmid |
| 2021 | GI | Grundkonzepte des Maschinellen Lernens fr die Grundschule - Algorithmen, Biases, Generalisierungsfehler. | Ute Schmid, Anja Grtig-Daugs, Linda Mller, Alexander Werner |
| 2021 | KI | Explanation as a Process: User-Centric Construction of Multi-level and Multi-modal Explanations. | Bettina Finzel, David E. Tafler, Stephan Scheele, Ute Schmid |
| 2020 | ESANN | Verifying Deep Learning-based Decisions for Facial Expression Recognition. | Ines Rieger, Rene Kollmann, Bettina Finzel, Dominik Seuss, Ute Schmid |
| 2020 | GI | Knstliche Intelligenz - Die dritte Welle. | Ute Schmid, Volker Tresp, Matthias Bethge, Kristian Kersting, Rainer Stiefelhagen |
| 2020 | KI | Expressive Explanations of DNNs by Combining Concept Analysis with ILP. | Johannes Rabold, Gesina Schwalbe, Ute Schmid |
| 2018 | ILP | Explaining Black-Box Classifiers with ILP - Empowering LIME with Aleph to Approximate Non-linear Decisions with Relational Rules. | Johannes Rabold, Michael Siebers, Ute Schmid |
| 2018 | ILP | Was the Year 2000 a Leap Year? Step-Wise Narrowing Theories with Metagol. | Michael Siebers, Ute Schmid |
| 2018 | KI | Inductive Programming as Approach to Comprehensible Machine Learning. | Ute Schmid |
| 2018 | KI | Intentional Forgetting in Artificial Intelligence Systems: Perspectives and Challenges. | Ingo J. Timm, Steffen Staab, Michael Siebers, Claudia Schon, Ute Schmid, Kai Sauerwald, Lukas Reuter, Marco Ragni, Claudia Niedere, Heiko Maus, Gabriele Kern-Isberner, Christian Jilek, Paulina Friemann, Thomas Eiter, Andreas Dengel, Hannah Dames, Tanja Bock, Jan Ole Berndt, Christoph Beierle |
| 2017 | BTW | Data Mining von multidimensionalen Qualittsdaten aus einer computerintegrierten industriellen Fertigung zur visuellen Analyse von komplexen Wirkzusammenhngen. | Frederick Birnbaum, Christian Moewes, Daniela Nicklas, Ute Schmid |
| 2017 | CogSci | The Impact of Presentation Order on Category Learning Strategies: Behavioral Data and Self-Reports. | Christina Zeller, Ute Schmid |
| 2017 | IJCAI | Computer Models Solving Intelligence Test Problems: Progress and Implications (Extended Abstract). | Jos Hernndez-Orallo, Fernando Martnez-Plumed, Ute Schmid, Michael Siebers, David L. Dowe |
| 2016 | ECAI | A Practical Approach to Fuse Shape and Appearance Information in a Gaussian Facial Action Estimation Framework. | Teena Hassan, Dominik Seuss, Johannes Wollenberg, Jens-Uwe Garbas, Ute Schmid |
| 2016 | GI | Gemeinsame mentale Modelle in der agilen Softwareentwicklung: Ein Ansatz zur Erstellung von Gestaltungsempfehlungen fr "gute" erfahrungsspezifische User Stories. | Daniel Hallmann, Ute Schmid, Rdiger von der Weth |
| 2016 | ICCBR | Automatic Generation of Analogous Problems to Help Resolving Misconceptions in an Intelligent Tutor System for Written Subtraction. | Christina Zeller, Ute Schmid |
| 2016 | ILP | How Does Predicate Invention Affect Human Comprehensibility? | Ute Schmid, Christina Zeller, Tarek R. Besold, Alireza Tamaddoni-Nezhad, Stephen H. Muggleton |
| 2015 | GI | Cognitive Systems: Goals, Approaches, Applications. | Ute Schmid |
| 2014 | KI | Applying Inductive Program Synthesis to Induction of Number Series A Case Study with IGOR2. | Jacqueline Hofmann, Emanuel Kitzelmann, Ute Schmid |
| 2012 | CogSci | Analogical Problem Solving: Insights from Verbal Reports. | Linn Gralla, Thora Tenbrink, Michael Siebers, Ute Schmid |
| 2012 | KI | Semi-analytic Natural Number Series Induction. | Michael Siebers, Ute Schmid |
| 2010 | ECAI | Data-Driven Detection of Recursive Program Schemes. | Martin Hofmann, Ute Schmid |
| 2010 | IC3K | Interleaving Forward Backward Feature Selection. | Michael Siebers, Ute Schmid |
| 2009 | IJCCI | Evolutionary Programming Guided by Analytically Generated Seeds. | Neil Crossley, Emanuel Kitzelmann, Martin Hofmann, Ute Schmid |
| 2008 | KI | Analysis and Evaluation of Inductive Programming Systems in a Higher-Order Framework. | Martin Hofmann, Emanuel Kitzelmann, Ute Schmid |
| 2007 | KI | Inductive Synthesis of Recursive Functional Programs. | Martin Hofmann, Andreas Hirschberger, Emanuel Kitzelmann, Ute Schmid |
| 2006 | KI | Solving Proportional Analogies by | Stephan Weller, Ute Schmid |
| 2002 | AISC | Inductive Synthesis of Functional Programs. | Emanuel Kitzelmann, Ute Schmid, Martin Mhlpfordt, Fritz Wysotzki |
| 2002 | KI | Integrating Function Application in State-Based Planning. | Ute Schmid, Marina Mller, Fritz Wysotzki |