315 citations · 629 across the 28 of their papers we have counts for
4 papers · 1 filter
DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision
Sungwon Han, Seungeon Lee, Fangzhao Wu +5
Algorithmic fairness has become an important machine learning problem, especially for mission-critical Web applications. This work presents a self-supervised model, called DualFair…
FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation
Chuhan Wu, Fangzhao Wu, Tao Qi +2
Contrastive learning is widely used for recommendation model learning, where selecting representative and informative negative samples is critical. Existing methods usually focus o…
Unified and Effective Ensemble Knowledge Distillation
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Ensemble knowledge distillation can extract knowledge from multiple teacher models and encode it into a single student model. Many existing methods learn and distill the student mo…
Semi-FairVAE: Semi-supervised Fair Representation Learning with Adversarial Variational Autoencoder
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Adversarial learning is a widely used technique in fair representation learning to remove the biases on sensitive attributes from data representations. It usually requires to incor…