4 papers
Performative Prediction with Selective Labels
Giovani Valdrighi, Isabel Valera, Marcos Medeiros Raimundo
Many social applications of machine learning exhibit performative effects: population behavior changes in response to deployed models. Performative prediction studies this interact…
Learning Pareto Stationary Fronts via Single-Pass Backpropagation
Elina Rojin Celik, Marcos Medeiros Raimundo, Isabel Valera
We propose MOSEL (Multi-Objective Stackelberg Efficient Learning), a framework for a posteriori multi-objective optimization (MOO) in deep neural networks that recovers a full fron…
Long-term Fairness with Selective Labels
Giovani Valdrighi, Isabel Valera, Marcos Medeiros Raimundo
Long-term fairness algorithms aim to satisfy fairness beyond static and short-term notions by accounting for the dynamics between decision-making policies and population behavior.…
Balancing Performance and Reject Inclusion: A Novel Confident Inlier Extrapolation Framework for Credit Scoring
Athyrson Machado Ribeiro, Marcos Medeiros Raimundo
Reject Inference (RI) methods aim to address sample bias by inferring missing repayment data for rejected credit applicants. Traditional approaches often assume that the behavior o…