3 papers
cs.LG2021
A causal learning framework for the analysis and interpretation of COVID-19 clinical data
Elisa Ferrari, Luna Gargani, Greta Barbieri +3
We present a workflow for clinical data analysis that relies on Bayesian Structure Learning (BSL), an unsupervised learning approach, robust to noise and biases, that allows to inc…
cs.LG2021
Addressing Fairness, Bias and Class Imbalance in Machine Learning: the FBI-loss
Elisa Ferrari, Davide Bacciu
Resilience to class imbalance and confounding biases, together with the assurance of fairness guarantees are highly desirable properties of autonomous decision-making systems with…
cs.LG2019
Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI)
Elisa Ferrari, Alessandra Retico, Davide Bacciu
Over the years, there has been growing interest in using Machine Learning techniques for biomedical data processing. When tackling these tasks, one needs to bear in mind that biome…