BRIGHT - Drift-Aware Demand Predictions for Taxi Networks (Extended Abstract). - Amal Saadallah; Luís Moreira-Matias; Ricardo Teixeira Sousa; Jihed Khiari; Erik Jenelius; João Gama 001 (ICDE, 2019)

Learning Ensembles in the Presence of Imbalanced Classes. - Amal Saadallah; Nico Piatkowski; Felix Finkeldey; Petra Wiederkehr; Katharina Morik (ICPRAM, 2019)

A Drift-Based Dynamic Ensemble Members Selection Using Clustering for Time Series Forecasting. - Amal Saadallah; Florian Priebe; Katharina Morik (ECML/PKDD (1), 2019)

Active Sampling for Learning Interpretable Surrogate Machine Learning Models. - Amal Saadallah; Katharina Morik (DSAA, 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)

BRIGHT - Drift-Aware Demand Predictions for Taxi Networks. - Amal Saadallah; Luís Moreira-Matias; Ricardo Teixeira Sousa; Jihed Khiari; Erik Jenelius; João Gama 001 (IEEE Trans. Knowl. Data Eng., 2020)

Online Ensemble Aggregation using Deep Reinforcement Learning for Time Series Forecasting. - Amal Saadallah; Katharina Morik (DSAA, 2021)

An Actor-Critic Ensemble Aggregation Model for Time-Series Forecasting. - Amal Saadallah; Maryam Tavakol; Katharina Morik (ICDE, 2021)

Meta-Adversarial Training of Neural Networks for Binary Classification. - Amal Saadallah; Katharina Morik (IJCNN, 2021)

Explainable Online Deep Neural Network Selection Using Adaptive Saliency Maps for Time Series Forecasting. - Amal Saadallah; Matthias Jakobs; Katharina Morik (ECML/PKDD (1), 2021)

Quality Assurance in Interlinked Manufacturing Processes. - Jochen Deuse; Katharina Morik; Amal Saadallah; Jan Büscher; Thorben Panusch (Mach. Learn. under Resour. Constraints Vol. 3 (3), 2022)

Simulation and Machine Learning. - Petra Wiederkehr; Katharina Morik; Amal Saadallah; Felix Finkeldey (Mach. Learn. under Resour. Constraints Vol. 3 (3), 2022)

Online Adaptive Multivariate Time Series Forecasting. - Amal Saadallah; Hanna Mykula; Katharina Morik (ECML/PKDD (6), 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)

Explainable online ensemble of deep neural network pruning for time series forecasting. - Amal Saadallah; Matthias Jakobs; Katharina Morik (Mach. Learn., 2022)

Explainable adaptation of time series forecasting. - Amal Saadallah (2022)

Online Explainable Model Selection for Time Series Forecasting. - Amal Saadallah (DSAA, 2023)

Tutorial: Interactive Adaptive Learning. - Mirko Bunse; Georg Krempl; Alaa Tharwat; Amal Saadallah (IAL@PKDD/ECML, 2023)

Explainable Adaptive Tree-based Model Selection for Time-Series Forecasting. - Matthias Jakobs; Amal Saadallah (ICDM, 2023)

Online Deep Hybrid Ensemble Learning for Time Series Forecasting. - Amal Saadallah; Matthias Jakobs (ECML/PKDD (5), 2023)

Online Adaptive Local Interpretable Tree Ensembles for Time Series Forecasting. - Amal Saadallah (ICDM (Workshops), 2024)

AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting. - Raphael Fischer 001; Amal Saadallah (KDD, 2024)

Online Explainable Forecasting using Regions of Competence. - Amal Saadallah; Matthias Jakobs (TempXAI@PKDD/ECML, 2024)

Online Explainable Ensemble of Tree Models Pruning for Time Series Forecasting. - Amal Saadallah (xAI (Late-breaking Work, Demos, Doctoral Consortium), 2024)

SHAP-Guided Regularization in Machine Learning Models. - Amal Saadallah (CoRR, 2025)