Amal Saadallah

According to our database, Amal Saadallah authored at least 23 papers between 2019 and 2024.

Collaborative distances:

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

2024

Online Explainable Ensemble of Tree Models Pruning for Time Series Forecasting.

Amal Saadallah

Proceedings of the Joint Proceedings of the xAI 2024 Late-breaking Work, 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

AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting.

Raphael Fischer, Amal Saadallah

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

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

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

Matthias Jakobs, Amal Saadallah

Proceedings of the IEEE International Conference on Data Mining, 2023

Tutorial: Interactive Adaptive Learning.

Mirko Bunse, Georg Krempl, 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), 2023

Online Explainable Model Selection for Time Series Forecasting.

Amal Saadallah

Proceedings of the 10th IEEE International Conference on Data Science and Advanced Analytics, 2023

2022

Explainable adaptation of time series forecasting.

Amal Saadallah

PhD thesis, 2022

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

Amal Saadallah, Matthias Jakobs, Katharina Morik

Mach. Learn., 2022

Simulation and sensor data fusion for machine learning application.

Amal Saadallah, Felix Finkeldey, Jens Buß, Katharina Morik, Petra Wiederkehr, Wolfgang Rhode

Adv. Eng. Informatics, 2022

Online Adaptive Multivariate Time Series Forecasting.

Amal Saadallah, Hanna Mykula, Katharina Morik

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

Simulation and Machine Learning.

Petra Wiederkehr, Katharina Morik, Amal Saadallah, Felix Finkeldey

Proceedings of the Machine Learning under Resource Constraints - Volume 3: Applications, 2022

Quality Assurance in Interlinked Manufacturing Processes.

Jochen Deuse, Katharina Morik, Amal Saadallah, Jan Büscher, Thorben Panusch

Proceedings of the Machine Learning under Resource Constraints - Volume 3: Applications, 2022

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

Meta-Adversarial Training of Neural Networks for Binary Classification.

Amal Saadallah, Katharina Morik

Proceedings of the International Joint Conference on Neural Networks, 2021

An Actor-Critic Ensemble Aggregation Model for Time-Series Forecasting.

Amal Saadallah, Maryam Tavakol, Katharina Morik

Proceedings of the 37th IEEE International Conference on Data Engineering, 2021

Online Ensemble Aggregation using Deep Reinforcement Learning for Time Series Forecasting.

Amal Saadallah, Katharina Morik

Proceedings of the 8th IEEE International Conference on Data Science and Advanced Analytics, 2021

2020

BRIGHT - Drift-Aware Demand Predictions for Taxi Networks.

Amal Saadallah, Luís Moreira-Matias, Ricardo Teixeira Sousa, Jihed Khiari, Erik Jenelius, João Gama

IEEE Trans. Knowl. Data Eng., 2020

Real-time prediction of process forces in milling operations using synchronized data fusion of simulation and sensor data.

Felix Finkeldey, Amal Saadallah, Petra Wiederkehr, Katharina Morik

Eng. Appl. Artif. Intell., 2020

Active Sampling for Learning Interpretable Surrogate Machine Learning Models.

Amal Saadallah, Katharina Morik

Proceedings of the 7th IEEE International Conference on Data Science and Advanced Analytics, 2020

2019

A Drift-Based Dynamic Ensemble Members Selection Using Clustering for Time Series Forecasting.

Amal Saadallah, Florian Priebe, Katharina Morik

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Learning Ensembles in the Presence of Imbalanced Classes.

Amal Saadallah, Nico Piatkowski, Felix Finkeldey, Petra Wiederkehr, Katharina Morik

Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods, 2019

BRIGHT - Drift-Aware Demand Predictions for Taxi Networks (Extended Abstract).

Amal Saadallah, Luís Moreira-Matias, Ricardo Teixeira Sousa, Jihed Khiari, Erik Jenelius, João Gama

Proceedings of the 35th IEEE International Conference on Data Engineering, 2019