UAI 2023poster14 citations
Fairness-aware class imbalanced learning on multiple subgroups
Davoud Ataee Tarzanagh, Bojian Hou, Boning Tong, Qi Long, Li Shen
Abstract
We present a novel Bayesian-based optimization framework that addresses the challenge of generalization in overparameterized models when dealing with imbalanced subgroups and limited samples per subgroup. Our proposed tri-level optimization framework utilizes
BibTeX
@InProceedings{pmlr-v216-tarzanagh23a,
title = {Fairness-aware class imbalanced learning on multiple subgroups},
author = {Tarzanagh, Davoud Ataee and Hou, Bojian and Tong, Boning and Long, Qi and Shen, Li},
booktitle = {Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence},
pages = {2123--2133},
year = {2023},
editor = {Evans, Robin J. and Shpitser, Ilya},
volume = {216},
series = {Proceedings of Machine Learning Research},
month = {31 Jul--04 Aug},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v216/tarzanagh23a/tarzanagh23a.pdf},
url = {https://proceedings.mlr.press/v216/tarzanagh23a.html},
abstract = {We present a novel Bayesian-based optimization framework that addresses the challenge of generalization in overparameterized models when dealing with imbalanced subgroups and limited samples per subgroup. Our proposed tri-level optimization framework utilizes