2 papers
cs.LG2026
Measuring the Sensitivity of Classification Models with the Error Sensitivity Profile
Andrea Maurino
The quality of training data is critical to the performance of machine learning models. In this paper, the Error Sensitivity Profile (ESP) is proposed. It quantifies the sensitivit…
cs.LG2025
PuckTrick: A Library for Making Synthetic Data More Realistic
Alessandra Agostini, Andrea Maurino, Blerina Spahiu
The increasing reliance on machine learning (ML) models for decision-making requires high-quality training data. However, access to real-world datasets is often restricted due to p…