The Good, the Bad and the Ugly: Augmenting a Black-Box Model with Expert Knowledge. - Raoul Heese; Michal Walczak; Lukas Morand; Dirk Helm; Michael Bortz (ICANN (Workshop), 2019)

Optimized data exploration applied to the simulation of a chemical process. - Raoul Heese; Michal Walczak; Tobias Seidel; Norbert Asprion; Michael Bortz (Comput. Chem. Eng., 2019)

Adaptive Sampling of Pareto Frontiers with Binary Constraints Using Regression and Classification. - Raoul Heese; Michael Bortz (ICPR, 2020)

CupNet - Pruning a Network for Geometric Data. - Raoul Heese; Lukas Morand; Dirk Helm; Michael Bortz (ICANN (4), 2021)

Calibrated Simplex Mapping Classification. - Raoul Heese; Michal Walczak; Michael Bortz; Jochen Schmid (CoRR, 2021)

Quantum Circuit Evolution on NISQ Devices. - Lukas Franken; Bogdan Georgiev; Sascha Mücke; Moritz Wolter; Raoul Heese; Christian Bauckhage; Nico Piatkowski (CEC, 2022)

Quantum Feature Selection. - Sascha Mücke; Raoul Heese; Sabine Müller 001; Moritz Wolter; Nico Piatkowski (CoRR, 2022)

Wavelet-packets for deepfake image analysis and detection. - Moritz Wolter; Felix Blanke; Raoul Heese; Jochen Garcke (Mach. Learn., 2022)

Representation of binary classification trees with binary features by quantum circuits. - Raoul Heese; Patricia Bickert; Astrid Elisa Niederle (Quantum, 2022)

Shapley Values with Uncertain Value Functions. - Raoul Heese; Sascha Mücke; Matthias Jakobs; Thore Gerlach; Nico Piatkowski (IDA, 2023)

Explainable Quantum Machine Learning. - Raoul Heese; Thore Gerlach; Sascha Mücke; Sabine Müller 001; Matthias Jakobs; Nico Piatkowski (CoRR, 2023)

A Quantum Optimization Case Study for a Transport Robot Scheduling Problem. - Dominik Leib; Tobias Seidel; Sven Jäger 001; Raoul Heese; Caitlin Isobel Jones; Abhishek Awasthi; Astrid Elisa Niederle; Michael Bortz (CoRR, 2023)

A Decomposition Method for the Hybrid Quantum-Classical Solution of the Number Partitioning Problem. - Zongji Li; Tobias Seidel; Michael Bortz; Raoul Heese (CoRR, 2023)

Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems. - Laura von Rüden; Sebastian Mayer; Katharina Beckh; Bogdan Georgiev; Sven Giesselbach; Raoul Heese; Birgit Kirsch; Julius Pfrommer; Annika Pick; Rajkumar Ramamurthy; Michal Walczak; Jochen Garcke; et al. (IEEE Trans. Knowl. Data Eng., 2023)

Feature selection on quantum computers. - Sascha Mücke; Raoul Heese; Sabine Müller 001; Moritz Wolter; Nico Piatkowski (Quantum Mach. Intell., 2023)

Quantum Wave Function Collapse for Procedural Content Generation. - Raoul Heese (IEEE Computer Graphics and Applications, 2024)

On the effects of biased quantum random numbers on the initialization of artificial neural networks. - Raoul Heese; Moritz Wolter; Sascha Mücke; Lukas Franken; Nico Piatkowski (Mach. Learn., 2024)

Quantum Adiabatic Generation of Human-Like Passwords. - Sascha Mücke; Raoul Heese; Thore Gerlach; David Biesner; Loong Kuan Lee; Nico Piatkowski (CoRR, 2025)

Efficient solution of the number partitioning problem on a quantum annealer: a hybrid quantum-classical decomposition approach. - Zongji Li; Tobias Seidel; Dominik Leib; Michael Bortz; Raoul Heese (J. Heuristics, 2025)

Explaining quantum circuits with Shapley values: towards explainable quantum machine learning. - Raoul Heese; Thore Gerlach; Sascha Mücke; Sabine Müller 001; Matthias Jakobs; Nico Piatkowski (Quantum Mach. Intell., 2025)