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Journal Articles
- 2022
[j2]Katharina Morik, Helena Kotthaus, Raphael Fischer, Sascha Mücke, Matthias Jakobs, Nico Piatkowski, Andreas Pauly, Lukas Heppe, Danny Heinrich:
Yes we care!-Certification for machine learning methods through the care label framework. Frontiers Artif. Intell. 5 (2022)- [j1]Amal Saadallah, Matthias Jakobs
, Katharina Morik:
Explainable online ensemble of deep neural network pruning for time series forecasting. Mach. Learn. 111(9): 3459-3487 (2022)
Conference and Workshop Papers
- 2024
- [c12]Amal Saadallah, Matthias Jakobs:
Online Explainable Forecasting using Regions of Competence. TempXAI@PKDD/ECML 2024: 7-11 - 2023
- [c11]Matthias Jakobs, Amal Saadallah:
Explainable Adaptive Tree-based Model Selection for Time-Series Forecasting. ICDM 2023: 180-189 - [c10]Raoul Heese, Sascha Mücke, Matthias Jakobs, Thore Gerlach, Nico Piatkowski:
Shapley Values with Uncertain Value Functions. IDA 2023: 156-168 - [c9]Amal Saadallah, Matthias Jakobs:
Online Deep Hybrid Ensemble Learning for Time Series Forecasting. ECML/PKDD (5) 2023: 156-171 - [c8]Sebastian Müller, Vanessa Toborek, Katharina Beckh, Matthias Jakobs, Christian Bauckhage, Pascal Welke:
An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning. ECML/PKDD (3) 2023: 462-478 - [c7]Katharina Beckh, Sebastian Müller, Matthias Jakobs, Vanessa Toborek, Hanxiao Tan, Raphael Fischer, Pascal Welke, Sebastian Houben, Laura von Rüden:
Harnessing Prior Knowledge for Explainable Machine Learning: An Overview. SaTML 2023: 450-463 - 2022
- [c6]Matthias Jakobs, Helena Kotthaus, Ines Röder, Maximilian Baritz:
SancScreen: Towards a Real-world Dataset for Evaluating Explainability Methods. LWDA 2022: 33-44 - [c5]Raphael Fischer, Matthias Jakobs, Sascha Mücke, Katharina Morik:
A Unified Framework for Assessing Energy Efficiency of Machine Learning. PKDD/ECML Workshops (1) 2022: 39-54 - 2021
- [c4]Amal Saadallah, Matthias Jakobs, Katharina Morik:
Explainable Online Deep Neural Network Selection Using Adaptive Saliency Maps for Time Series Forecasting. ECML/PKDD (1) 2021: 404-420 - 2020
- [c3]Raphael Fischer, Matthias Jakobs, Sascha Mücke, Katharina Morik:
Solving Abstract Reasoning Tasks with Grammatical Evolution. LWDA 2020: 6-10 - 2019
- [c2]Thomas Kirks, Jana Jost, Tim Uhlott, Julian Püth, Matthias Jakobs:
Evaluation of the Application of Smart Glasses for Decentralized Control Systems in Logistics. ITSC 2019: 4470-4476 - 2018
- [c1]Thomas Kirks, Jana Jost, Tim Uhlott, Matthias Jakobs:
Towards Complex Adaptive Control Systems for Human-Robot-Interaction in Intralogistics. ITSC 2018: 2968-2973
Editorship
- 2024
- [e1]Zahraa S. Abdallah, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier, Matthias Jakobs, Emmanuel Müller, Maximilian Muschalik, Panagiotis Papapetrou, Amal Saadallah, George Tzagkarakis:
Proceedings of the Workshop on Explainable AI for Time Series and Data Streams (TempXAI 2024) co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2024), Vilnius, Lithuania, September 9th, 2024. CEUR Workshop Proceedings 3761, CEUR-WS.org 2024 [contents]
Informal and Other Publications
- 2024
- [i8]Matthias Jakobs, Amal Saadallah:
Explainable Adaptive Tree-based Model Selection for Time Series Forecasting. CoRR abs/2401.01124 (2024) - [i7]Matthias Jakobs, Thomas Liebig:
AALF: Almost Always Linear Forecasting. CoRR abs/2409.10142 (2024) - 2023
- [i6]Raoul Heese, Sascha Mücke, Matthias Jakobs, Thore Gerlach, Nico Piatkowski:
Shapley Values with Uncertain Value Functions. CoRR abs/2301.08086 (2023) - [i5]Raoul Heese, Thore Gerlach, Sascha Mücke, Sabine Müller, Matthias Jakobs, Nico Piatkowski:
Explainable Quantum Machine Learning. CoRR abs/2301.09138 (2023) - [i4]Raphael Fischer, Matthias Jakobs, Katharina Morik:
Energy Efficiency Considerations for Popular AI Benchmarks. CoRR abs/2304.08359 (2023) - [i3]Sebastian Müller, Vanessa Toborek, Katharina Beckh, Matthias Jakobs, Christian Bauckhage, Pascal Welke:
An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning. CoRR abs/2306.15786 (2023) - 2021
- [i2]Katharina Beckh, Sebastian Müller, Matthias Jakobs, Vanessa Toborek, Hanxiao Tan, Raphael Fischer, Pascal Welke, Sebastian Houben, Laura von Rüden:
Explainable Machine Learning with Prior Knowledge: An Overview. CoRR abs/2105.10172 (2021) - [i1]Katharina Morik, Helena Kotthaus, Lukas Heppe, Danny Heinrich, Raphael Fischer, Sascha Mücke, Andreas Pauly, Matthias Jakobs, Nico Piatkowski:
Yes We Care! - Certification for Machine Learning Methods through the Care Label Framework. CoRR abs/2105.10197 (2021)
Coauthor Index
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