Maxence Ernoult, Fabrice Normandin, et al.
ICML 2022
Bias mitigators can reduce algorithmic bias in machine learning models, but their effect on fairness is often not stable across different data splits. A popular approach to train more stable models is ensemble learning. We built an open-source library enabling the modular composition of 10mitigators, 4ensembles, and their corresponding hyperparameters. We empirically explored the space of combinations on 13 datasets and distilled the results into a guidance diagram for practitioners.
Maxence Ernoult, Fabrice Normandin, et al.
ICML 2022
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Natalia Martinez Gil, Dhaval Patel, et al.
UAI 2024
Shubhi Asthana, Pawan Chowdhary, et al.
KDD 2021