6 papers
Learnability with Partial Labels and Adaptive Nearest Neighbors
Nicolas A. Errandonea, Santiago Mazuelas, Jose A. Lozano +1
Prior work on partial labels learning (PLL) has shown that learning is possible even when each instance is associated with a bag of labels, rather than a single accurate but costly…
Safe Fairness Guarantees Without Demographics in Classification: Spectral Uncertainty Set Perspective
Ainhize Barrainkua, Santiago Mazuelas, Novi Quadrianto +1
As automated classification systems become increasingly prevalent, concerns have emerged over their potential to reinforce and amplify existing societal biases. In the light of thi…
Dissecting Performative Prediction: A Comprehensive Survey
Thomas Kehrenberg, Javier Sanguino, Jose A. Lozano +1
The field of performative prediction had its beginnings in 2020 with the seminal paper "Performative Prediction" by Perdomo et al., which established a novel machine learning setup…
Revisiting (Un)Fairness in Recourse by Minimizing Worst-Case Social Burden
Ainhize Barrainkua, Giovanni De Toni, Jose Antonio Lozano +1
Machine learning based predictions are increasingly used in sensitive decision-making applications that directly affect our lives. This has led to extensive research into ensuring…
Strategically Deceptive Model Deployment in Performative Prediction
Javier Sanguino Bautiste, Thomas Kehrenberg, Jose A. Lozano +1
Machine Learning systems are increasingly deployed in decision-making settings that shape user behavior and, in turn, the data on which future decisions are based. Performative Pre…
Dancing in the Shadows: Harnessing Ambiguity for Fairer Classifiers
Ainhize Barrainkua, Paula Gordaliza, Jose A. Lozano +1
This paper introduces a novel approach to bolster algorithmic fairness in scenarios where sensitive information is only partially known. In particular, we propose to leverage insta…