Publications [#356485] of Guillermo Sapiro

Papers Published

1. Martinez, N; Bertran, M; Sapiro, G, Minimax pareto fairness: A multi objective perspective, 37th International Conference on Machine Learning, ICML 2020, vol. PartF168147-9 (January, 2020), pp. 6711-6720, ISBN 9781713821120
   (last updated on 2025/02/02)

   Abstract:
   In this work we formulate and formally characterize group fairness as a multi-objective optimization problem, where each sensitive group risk is a separate objective. We propose a fairness criterion where a classifier achieves minimax risk and is Pareto-efficient w.r.t. all groups, avoiding unnecessary harm, and can lead to the best zero-gap model if policy dictates so. We provide a simple optimization algorithm compatible with deep neural networks to satisfy these constraints. Since our method does not require test-Time access to sensitive attributes, it can be applied to reduce worst-case classification errors between outcomes in unbalanced classification problems. We test the proposed methodology on real case-studies of predicting income, ICU patient mortality, skin lesions classification, and assessing credit risk, demonstrating how our framework compares favorably to other approaches.