2 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 1 cited
Adversarial Reweighting Guided by Wasserstein Distance for Bias Mitigation
Xuan Zhao, Simone Fabbrizzi, Paula Reyero Lobo +4
The unequal representation of different groups in a sample population can lead to discrimination of minority groups when machine learning models make automated decisions. To addres…
cs.LG2019★ 2 cited
Self-Paced Multi-Label Learning with Diversity
Seyed Amjad Seyedi, S. Siamak Ghodsi, Fardin Akhlaghian +2
The major challenge of learning from multi-label data has arisen from the overwhelming size of label space which makes this problem NP-hard. This problem can be alleviated by gradu…