1 citations · 1 across the 4 of their papers we have counts for
9 papers
Personalized Causal Recourse: A Human-In-The-Loop Approach
Denise Tampieri, Giovanni De Toni, Paolo Giudici
Algorithmic recourse addresses the challenge of providing tailored recommendations to users affected by unfavorable machine learning decisions, in potentially high-stakes scenarios…
Divide et Calibra: Multiclass Local Calibration via Vector Quantization
Cesare Barbera, Lorenzo Perini, Giovanni De Toni +2
Accurate and well-calibrated Machine Learning (ML) models are mandatory in high-stakes settings, yet effective multiclass calibration remains challenging: global approaches assume…
Multiclass Local Calibration with the Jensen-Shannon Distance
Cesare Barbera, Lorenzo Perini, Giovanni De Toni +2
Developing trustworthy Machine Learning (ML) models requires their predicted probabilities to be well-calibrated, meaning they should reflect true-class frequencies. Among calibrat…
With a Little Help From My Friends: Collective Manipulation in Risk-Controlling Recommender Systems
Giovanni De Toni, Cristian Consonni, Erasmo Purificato +2
Recommendation systems have become central gatekeepers of online information, shaping user behaviour across a wide range of activities. In response, users increasingly organize and…
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…
To Ask or Not to Ask: Learning to Require Human Feedback
Andrea Pugnana, Giovanni De Toni, Cesare Barbera +3
Developing decision-support systems that complement human performance in classification tasks remains an open challenge. A popular approach, Learning to Defer (LtD), allows a Machi…