10 citations · 27 across the 11 of their papers we have counts for
10 papers · 1 filter
Intersectional Divergence: Measuring Fairness in Regression
Joe Germino, Nuno Moniz, Nitesh V. Chawla
Fairness in machine learning research is commonly framed in the context of classification tasks, leaving critical gaps in regression. In this paper, we propose a novel approach to…
Automated Privacy-Preserving Techniques via Meta-Learning
Tânia Carvalho, Nuno Moniz, Luís Antunes
Sharing private data for learning tasks is pivotal for transparent and secure machine learning applications. Many privacy-preserving techniques have been proposed for this task aim…
Synthetic Data Outliers: Navigating Identity Disclosure
Carolina Trindade, Luís Antunes, Tânia Carvalho +1
Multiple synthetic data generation models have emerged, among which deep learning models have become the vanguard due to their ability to capture the underlying characteristics of…
AnyLoss: Transforming Classification Metrics into Loss Functions
Doheon Han, Nuno Moniz, Nitesh V Chawla
Many evaluation metrics can be used to assess the performance of models in binary classification tasks. However, most of them are derived from a confusion matrix in a non-different…
Fast Explanations via Policy Gradient-Optimized Explainer
Deng Pan, Nuno Moniz, Nitesh Chawla
The challenge of delivering efficient explanations is a critical barrier that prevents the adoption of model explanations in real-world applications. Existing approaches often depe…
Conformalized Selective Regression
Anna Sokol, Nuno Moniz, Nitesh Chawla
Should prediction models always deliver a prediction? In the pursuit of maximum predictive performance, critical considerations of reliability and fairness are often overshadowed,…