12 citations · 23 across the 12 of their papers we have counts for
4 papers · 1 filter
Sustainable Machine Learning Retraining: Optimizing Energy Efficiency Without Compromising Accuracy
Lorena Poenaru-Olaru, June Sallou, Luis Cruz +2
The reliability of machine learning (ML) software systems is heavily influenced by changes in data over time. For that reason, ML systems require regular maintenance, typically bas…
FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learning
Binghui Zhang, Luis Mares De La Cruz, Binghui Wang
Federated Learning (FL) is an emerging decentralized learning paradigm that can partly address the privacy concern that cannot be handled by traditional centralized and distributed…
Data vs. Model Machine Learning Fairness Testing: An Empirical Study
Arumoy Shome, Luis Cruz, Arie van Deursen
Although several fairness definitions and bias mitigation techniques exist in the literature, all existing solutions evaluate fairness of Machine Learning (ML) systems after the tr…
Is Your Anomaly Detector Ready for Change? Adapting AIOps Solutions to the Real World
Lorena Poenaru-Olaru, Natalia Karpova, Luis Cruz +2
Anomaly detection techniques are essential in automating the monitoring of IT systems and operations. These techniques imply that machine learning algorithms are trained on operati…