Matthias Jakobs

29 papers A* 1B 2Journal 15Unranked 10
YearRankTypeTitle / Venue / Authors
2026 J jnl
AALF: Almost Always Linear Forecasting.
Mach. Learn.
Matthias Jakobs, Thomas Liebig
2026 J jnl
Interpretable rules for online failure prediction: a case study on metro do porto datasets.
Int. J. Data Sci. Anal.
Matthias Jakobs, Bruno Veloso, João Gama
2025 J jnl
Decentralized Time Series Classification with ROCKET Features.
CoRR
Bruno Casella, Matthias Jakobs, Marco Aldinucci, Sebastian Buschjäger
2025 J jnl
Explaining quantum circuits with Shapley values: towards explainable quantum machine learning.
Quantum Mach. Intell.
Raoul Heese, Thore Gerlach, Sascha Mücke, Sabine Müller, Matthias Jakobs, Nico Piatkowski
2025 J jnl
Interpretable Rules for Online Failure Prediction: A Case Study on the Metro do Porto dataset.
CoRR
Matthias Jakobs, Bruno Veloso, João Gama
2024 J jnl
AALF: Almost Always Linear Forecasting.
CoRR
Matthias Jakobs, Thomas Liebig
2024 J jnl
Explainable Adaptive Tree-based Model Selection for Time Series Forecasting.
CoRR
Matthias Jakobs, Amal Saadallah
2024 B conf
Federated Time Series Classification with ROCKET features.
ESANN
Bruno Casella, Matthias Jakobs, Marco Aldinucci, Sebastian Buschjäger
2024 — conf
Online Explainable Forecasting using Regions of Competence.
TempXAI@PKDD/ECML
Amal Saadallah, Matthias Jakobs
2024 — ed.
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.
TempXAI@PKDD/ECML
Zahraa S. Abdallah, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier, Matthias Jakobs, Emmanuel Müller, Maximilian Muschalik, Panagiotis Papapetrou, Amal Saadallah, George Tzagkarakis
2023 — conf
An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning.
ECML/PKDD (3)
Sebastian Müller, Vanessa Toborek, Katharina Beckh, Matthias Jakobs, Christian Bauckhage, Pascal Welke
2023 J jnl
An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning.
CoRR
Sebastian Müller, Vanessa Toborek, Katharina Beckh, Matthias Jakobs, Christian Bauckhage, Pascal Welke
2023 J jnl
Energy Efficiency Considerations for Popular AI Benchmarks.
CoRR
Raphael Fischer, Matthias Jakobs, Katharina Morik
2023 A* conf
Explainable Adaptive Tree-based Model Selection for Time-Series Forecasting.
ICDM
Matthias Jakobs, Amal Saadallah
2023 J jnl
Explainable Quantum Machine Learning.
CoRR
Raoul Heese, Thore Gerlach, Sascha Mücke, Sabine Müller, Matthias Jakobs, Nico Piatkowski
2023 — conf
Harnessing Prior Knowledge for Explainable Machine Learning: An Overview.
SaTML
Katharina Beckh, Sebastian Müller, Matthias Jakobs, Vanessa Toborek, Hanxiao Tan, Raphael Fischer, Pascal Welke, Sebastian Houben, Laura von Rüden
2023 — conf
Online Deep Hybrid Ensemble Learning for Time Series Forecasting.
ECML/PKDD (5)
Amal Saadallah, Matthias Jakobs
2023 B conf
Shapley Values with Uncertain Value Functions.
IDA
Raoul Heese, Sascha Mücke, Matthias Jakobs, Thore Gerlach, Nico Piatkowski
2023 J jnl
Shapley Values with Uncertain Value Functions.
CoRR
Raoul Heese, Sascha Mücke, Matthias Jakobs, Thore Gerlach, Nico Piatkowski
2022 — conf
A Unified Framework for Assessing Energy Efficiency of Machine Learning.
PKDD/ECML Workshops (1)
Raphael Fischer, Matthias Jakobs, Sascha Mücke, Katharina Morik
2022 J jnl
Explainable online ensemble of deep neural network pruning for time series forecasting.
Mach. Learn.
Amal Saadallah, Matthias Jakobs, Katharina Morik
2022 — conf
SancScreen: Towards a Real-world Dataset for Evaluating Explainability Methods.
LWDA
Matthias Jakobs, Helena Kotthaus, Ines Röder, Maximilian Baritz
2022 J jnl
Yes we care!-Certification for machine learning methods through the care label framework.
Frontiers Artif. Intell.
Katharina Morik, Helena Kotthaus, Raphael Fischer, Sascha Mücke, Matthias Jakobs, Nico Piatkowski, Andreas Pauly, Lukas Heppe, Danny Heinrich
2021 J jnl
Explainable Machine Learning with Prior Knowledge: An Overview.
CoRR
Katharina Beckh, Sebastian Müller, Matthias Jakobs, Vanessa Toborek, Hanxiao Tan, Raphael Fischer, Pascal Welke, Sebastian Houben, Laura von Rüden
2021 — conf
Explainable Online Deep Neural Network Selection Using Adaptive Saliency Maps for Time Series Forecasting.
ECML/PKDD (1)
Amal Saadallah, Matthias Jakobs, Katharina Morik
2021 J jnl
Yes We Care! - Certification for Machine Learning Methods through the Care Label Framework.
CoRR
Katharina Morik, Helena Kotthaus, Lukas Heppe, Danny Heinrich, Raphael Fischer, Sascha Mücke, Andreas Pauly, Matthias Jakobs, Nico Piatkowski
2020 — conf
Solving Abstract Reasoning Tasks with Grammatical Evolution.
LWDA
Raphael Fischer, Matthias Jakobs, Sascha Mücke, Katharina Morik
2019 — conf
Evaluation of the Application of Smart Glasses for Decentralized Control Systems in Logistics.
ITSC
Thomas Kirks, Jana Jost, Tim Uhlott, Julian Püth, Matthias Jakobs
2018 — conf
Towards Complex Adaptive Control Systems for Human-Robot-Interaction in Intralogistics.
ITSC
Thomas Kirks, Jana Jost, Tim Uhlott, Matthias Jakobs