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ORCIDAmal Saadallah
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- affiliation: Technical University of Dortmund, Germany
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2020 – today
- 2024
[c16]Raphael Fischer
, Amal Saadallah:
AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting. KDD 2024: 806-815- [c15]Amal Saadallah, Matthias Jakobs:
Online Explainable Forecasting using Regions of Competence. TempXAI@PKDD/ECML 2024: 7-11 - [c14]Amal Saadallah:
Online Explainable Ensemble of Tree Models Pruning for Time Series Forecasting. xAI (Late-breaking Work, Demos, Doctoral Consortium) 2024: 89-96 - [e3]Mirko Bunse, Marek Herde, Georg Krempl, Vincent Lemaire, Alaa Tharwat, Minh Tuan Pham, Amal Saadallah:
Proceedings of the Workshop on Interactive Adaptive Learning co-located with 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 3770, CEUR-WS.org 2024 [contents] - [e2]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] - [i2]Matthias Jakobs, Amal Saadallah:
Explainable Adaptive Tree-based Model Selection for Time Series Forecasting. CoRR abs/2401.01124 (2024) - 2023
- [c13]Amal Saadallah:
Online Explainable Model Selection for Time Series Forecasting. DSAA 2023: 1-10 - [c12]Mirko Bunse, Georg Krempl, Alaa Tharwat, Amal Saadallah:
Tutorial: Interactive Adaptive Learning. IAL@PKDD/ECML 2023: 1-8 - [c11]Matthias Jakobs, Amal Saadallah:
Explainable Adaptive Tree-based Model Selection for Time-Series Forecasting. ICDM 2023: 180-189 - [c10]Amal Saadallah, Matthias Jakobs:
Online Deep Hybrid Ensemble Learning for Time Series Forecasting. ECML/PKDD (5) 2023: 156-171 - [e1]Mirko Bunse, Barbara Hammer, Georg Krempl, Vincent Lemaire, Alaa Tharwat, Amal Saadallah:
Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2023), Torino, Italy, September 22nd, 2023. CEUR Workshop Proceedings 3470, CEUR-WS.org 2023 [contents] - [i1]Raphael Fischer, Amal Saadallah:
AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting. CoRR abs/2312.13038 (2023) - 2022
- [b1]Amal Saadallah:
Explainable adaptation of time series forecasting. Technical University of Dortmund, Germany, 2022 - [j4]Amal Saadallah, Felix Finkeldey, Jens Buß, Katharina Morik, Petra Wiederkehr, Wolfgang Rhode:
Simulation and sensor data fusion for machine learning application. Adv. Eng. Informatics 52: 101600 (2022) - [j3]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) - [c9]Amal Saadallah, Hanna Mykula, Katharina Morik:
Online Adaptive Multivariate Time Series Forecasting. ECML/PKDD (6) 2022: 19-35 - [p2]Jochen Deuse, Katharina Morik, Amal Saadallah, Jan Büscher, Thorben Panusch:
Quality Assurance in Interlinked Manufacturing Processes. Mach. Learn. under Resour. Constraints Vol. 3 (3) 2022: 114-135 - [p1]Petra Wiederkehr, Katharina Morik, Amal Saadallah, Felix Finkeldey:
Simulation and Machine Learning. Mach. Learn. under Resour. Constraints Vol. 3 (3) 2022: 157-179 - 2021
- [c8]Amal Saadallah, Katharina Morik:
Online Ensemble Aggregation using Deep Reinforcement Learning for Time Series Forecasting. DSAA 2021: 1-8 - [c7]Amal Saadallah, Maryam Tavakol, Katharina Morik:
An Actor-Critic Ensemble Aggregation Model for Time-Series Forecasting. ICDE 2021: 2255-2260 - [c6]Amal Saadallah, Katharina Morik:
Meta-Adversarial Training of Neural Networks for Binary Classification. IJCNN 2021: 1-7 - [c5]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
- [j2]Felix Finkeldey, Amal Saadallah, Petra Wiederkehr, Katharina Morik:
Real-time prediction of process forces in milling operations using synchronized data fusion of simulation and sensor data. Eng. Appl. Artif. Intell. 94: 103753 (2020) - [j1]Amal Saadallah, Luís Moreira-Matias, Ricardo Teixeira Sousa, Jihed Khiari, Erik Jenelius, João Gama:
BRIGHT - Drift-Aware Demand Predictions for Taxi Networks. IEEE Trans. Knowl. Data Eng. 32(2): 234-245 (2020) - [c4]Amal Saadallah, Katharina Morik:
Active Sampling for Learning Interpretable Surrogate Machine Learning Models. DSAA 2020: 264-272
2010 – 2019
- 2019
- [c3]Amal Saadallah, Luís Moreira-Matias, Ricardo Teixeira Sousa, Jihed Khiari, Erik Jenelius, João Gama:
BRIGHT - Drift-Aware Demand Predictions for Taxi Networks (Extended Abstract). ICDE 2019: 2145-2146 - [c2]Amal Saadallah, Nico Piatkowski, Felix Finkeldey, Petra Wiederkehr, Katharina Morik:
Learning Ensembles in the Presence of Imbalanced Classes. ICPRAM 2019: 866-873 - [c1]Amal Saadallah, Florian Priebe, Katharina Morik:
A Drift-Based Dynamic Ensemble Members Selection Using Clustering for Time Series Forecasting. ECML/PKDD (1) 2019: 678-694
Coauthor Index
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