Matthias Jakobs

According to our database, Matthias Jakobs authored at least 18 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

AALF: Almost Always Linear Forecasting.

Matthias Jakobs, Thomas Liebig

CoRR, 2024

Online Explainable Forecasting using Regions of Competence.

Amal Saadallah, Matthias Jakobs

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), 2024

2023

Energy Efficiency Considerations for Popular AI Benchmarks.

Raphael Fischer, Matthias Jakobs, Katharina Morik

CoRR, 2023

Explainable Quantum Machine Learning.

Raoul Heese, Thore Gerlach, Sascha Mücke, Sabine Müller, Matthias Jakobs, Nico Piatkowski

CoRR, 2023

Harnessing Prior Knowledge for Explainable Machine Learning: An Overview.

Katharina Beckh, Sebastian Müller, Matthias Jakobs, Vanessa Toborek, Hanxiao Tan, Raphael Fischer, Pascal Welke, Sebastian Houben, Laura von Rüden

Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning, 2023

Online Deep Hybrid Ensemble Learning for Time Series Forecasting.

Amal Saadallah, Matthias Jakobs

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning.

Sebastian Müller, Vanessa Toborek, Katharina Beckh, Matthias Jakobs, Christian Bauckhage, Pascal Welke

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Shapley Values with Uncertain Value Functions.

Raoul Heese, Sascha Mücke, Matthias Jakobs, Thore Gerlach, Nico Piatkowski

Proceedings of the Advances in Intelligent Data Analysis XXI, 2023

Explainable Adaptive Tree-based Model Selection for Time-Series Forecasting.

Matthias Jakobs, Amal Saadallah

Proceedings of the IEEE International Conference on Data Mining, 2023

2022

Explainable online ensemble of deep neural network pruning for time series forecasting.

Amal Saadallah, Matthias Jakobs, Katharina Morik

Mach. Learn., 2022

Yes we care!-Certification for machine learning methods through the care label framework.

Katharina Morik, Helena Kotthaus, Raphael Fischer, Sascha Mücke, Matthias Jakobs, Nico Piatkowski, Andreas Pauly, Lukas Heppe, Danny Heinrich

Frontiers Artif. Intell., 2022

A Unified Framework for Assessing Energy Efficiency of Machine Learning.

Raphael Fischer, Matthias Jakobs, Sascha Mücke, Katharina Morik

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022

SancScreen: Towards a Real-world Dataset for Evaluating Explainability Methods.

Matthias Jakobs, Helena Kotthaus, Ines Röder, Maximilian Baritz

Proceedings of the LWDA 2022 Workshops: FGWM, 2022

2021

Explainable Machine Learning with Prior Knowledge: An Overview.

Katharina Beckh, Sebastian Müller, Matthias Jakobs, Vanessa Toborek, Hanxiao Tan, Raphael Fischer, Pascal Welke, Sebastian Houben, Laura von Rüden

CoRR, 2021

Explainable Online Deep Neural Network Selection Using Adaptive Saliency Maps for Time Series Forecasting.

Amal Saadallah, Matthias Jakobs, Katharina Morik

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

2020

Solving Abstract Reasoning Tasks with Grammatical Evolution.

Raphael Fischer, Matthias Jakobs, Sascha Mücke, Katharina Morik

Proceedings of the Conference "Lernen, 2020

2019

Evaluation of the Application of Smart Glasses for Decentralized Control Systems in Logistics.

Thomas Kirks, Jana Jost, Tim Uhlott, Julian Püth, Matthias Jakobs

Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference, 2019

2018

Towards Complex Adaptive Control Systems for Human-Robot-Interaction in Intralogistics.

Thomas Kirks, Jana Jost, Tim Uhlott, Matthias Jakobs

Proceedings of the 21st International Conference on Intelligent Transportation Systems, 2018