Default Prediction Using Network Based Features. - Lorena Poenaru-Olaru; Judith Redi; Arthur Hovanesyan; Huijuan Wang (COMPLEX NETWORKS, 2021)

AutoML: towards automation of machine learning systems maintainability. - Lorena Poenaru-Olaru (Middleware Doctoral Symposium, 2021)

Are Concept Drift Detectors Reliable Alarming Systems? - A Comparative Study. - Lorena Poenaru-Olaru; Luis Cruz 002; Arie van Deursen; Jan S. Rellermeyer (IEEE Big Data, 2022)

Maintaining and Monitoring AIOps Models Against Concept Drift. - Lorena Poenaru-Olaru; Luis Cruz 002; Jan S. Rellermeyer; Arie van Deursen (CAIN, 2023)

Retrain AI Systems Responsibly! Use Sustainable Concept Drift Adaptation Techniques. - Lorena Poenaru-Olaru; June Sallou; Luis Cruz 002; Jan S. Rellermeyer; Arie van Deursen (GREENS@ICSE, 2023)

Maintenance Techniques for Anomaly Detection AIOps Solutions. - Lorena Poenaru-Olaru; Natalia Karpova; Luis Cruz 002; Jan S. Rellermeyer; Arie van Deursen (CoRR, 2023)

Is Your Anomaly Detector Ready for Change? Adapting AIOps Solutions to the Real World. - Lorena Poenaru-Olaru; Natalia Karpova; Luis Cruz 002; Jan S. Rellermeyer; Arie van Deursen (CAIN, 2024)

McUDI: Model-Centric Unsupervised Degradation Indicator for Failure Prediction AIOps Solutions. - Lorena Poenaru-Olaru; Luis Cruz 002; Jan S. Rellermeyer; Arie van Deursen (CoRR, 2024)

Improving the Reliability of Failure Prediction Models through Concept Drift Monitoring. - Lorena Poenaru-Olaru; Luis Cruz 002; Jan S. Rellermeyer; Arie van Deursen (DeepTest, 2025)

Sustainable Machine Learning Retraining: Optimizing Energy Efficiency Without Compromising Accuracy. - Lorena Poenaru-Olaru; June Sallou; Luis Cruz 002; Jan S. Rellermeyer; Arie van Deursen (CoRR, 2025)