Towards Complex Adaptive Control Systems for Human-Robot-Interaction in Intralogistics. - Thomas Kirks; Jana Jost; Tim Uhlott; Matthias Jakobs (ITSC, 2018)

Evaluation of the Application of Smart Glasses for Decentralized Control Systems in Logistics. - Thomas Kirks; Jana Jost; Tim Uhlott; Julian Püth; Matthias Jakobs (ITSC, 2019)

Solving Abstract Reasoning Tasks with Grammatical Evolution. - Raphael Fischer 001; Matthias Jakobs; Sascha Mücke; Katharina Morik (LWDA, 2020)

Explainable Online Deep Neural Network Selection Using Adaptive Saliency Maps for Time Series Forecasting. - Amal Saadallah; Matthias Jakobs; Katharina Morik (ECML/PKDD (1), 2021)

Explainable Machine Learning with Prior Knowledge: An Overview. - Katharina Beckh; Sebastian Müller; Matthias Jakobs; Vanessa Toborek; Hanxiao Tan; Raphael Fischer 001; Pascal Welke; Sebastian Houben; Laura von Rüden (CoRR, 2021)

SancScreen: Towards a Real-world Dataset for Evaluating Explainability Methods. - Matthias Jakobs; Helena Kotthaus; Ines Röder; Maximilian Baritz (LWDA, 2022)

A Unified Framework for Assessing Energy Efficiency of Machine Learning. - Raphael Fischer 001; Matthias Jakobs; Sascha Mücke; Katharina Morik (PKDD/ECML Workshops (1), 2022)

Yes we care!-Certification for machine learning methods through the care label framework. - Katharina Morik; Helena Kotthaus; Raphael Fischer 001; Sascha Mücke; Matthias Jakobs; Nico Piatkowski; Andreas Pauly; Lukas Heppe; Danny Heinrich (Frontiers Artif. Intell., 2022)

Explainable online ensemble of deep neural network pruning for time series forecasting. - Amal Saadallah; Matthias Jakobs; Katharina Morik (Mach. Learn., 2022)

Explainable Adaptive Tree-based Model Selection for Time-Series Forecasting. - Matthias Jakobs; Amal Saadallah (ICDM, 2023)

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

An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning. - Sebastian Müller; Vanessa Toborek; Katharina Beckh; Matthias Jakobs; Christian Bauckhage; Pascal Welke (ECML/PKDD (3), 2023)

Online Deep Hybrid Ensemble Learning for Time Series Forecasting. - Amal Saadallah; Matthias Jakobs (ECML/PKDD (5), 2023)

Harnessing Prior Knowledge for Explainable Machine Learning: An Overview. - Katharina Beckh; Sebastian Müller; Matthias Jakobs; Vanessa Toborek; Hanxiao Tan; Raphael Fischer 001; Pascal Welke; Sebastian Houben; Laura von Rüden (SaTML, 2023)

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

Energy Efficiency Considerations for Popular AI Benchmarks. - Raphael Fischer 001; Matthias Jakobs; Katharina Morik (CoRR, 2023)

Federated Time Series Classification with ROCKET features. - Bruno Casella; Matthias Jakobs; Marco Aldinucci; Sebastian Buschjäger (ESANN, 2024)

Online Explainable Forecasting using Regions of Competence. - Amal Saadallah; Matthias Jakobs (TempXAI@PKDD/ECML, 2024)

AALF: Almost Always Linear Forecasting. - Matthias Jakobs; Thomas Liebig (CoRR, 2024)

Interpretable Rules for Online Failure Prediction: A Case Study on the Metro do Porto dataset. - Matthias Jakobs; Bruno Veloso; João Gama 001 (CoRR, 2025)

Decentralized Time Series Classification with ROCKET Features. - Bruno Casella; Matthias Jakobs; Marco Aldinucci; Sebastian Buschjäger (CoRR, 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)