2 citations · 3 across the 6 of their papers we have counts for
6 papers
A Deep Latent Factor Graph Clustering with Fairness-Utility Trade-off Perspective
Siamak Ghodsi, Amjad Seyedi, Tai Le Quy +2
Fair graph clustering seeks partitions that respect network structure while maintaining proportional representation across sensitive groups, with applications spanning community de…
Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering
Siamak Ghodsi, Seyed Amjad Seyedi, Eirini Ntoutsi
Conventional fair graph clustering methods face two primary challenges: i) They prioritize balanced clusters at the expense of cluster cohesion by imposing rigid constraints, ii) E…
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…
Affinity Clustering Framework for Data Debiasing Using Pairwise Distribution Discrepancy
Siamak Ghodsi, Eirini Ntoutsi
Group imbalance, resulting from inadequate or unrepresentative data collection methods, is a primary cause of representation bias in datasets. Representation bias can exist with re…
Context matters for fairness -- a case study on the effect of spatial distribution shifts
Siamak Ghodsi, Harith Alani, Eirini Ntoutsi
With the ever growing involvement of data-driven AI-based decision making technologies in our daily social lives, the fairness of these systems is becoming a crucial phenomenon. Ho…
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…