4 papers · 1 filter
Interpretable and Fair Generalized Additive Neural Networks via Multi-objective Learning
Ziming Wang, Changwu Huang, Ke Tang +2
Interpretability and fairness are two of the most emphasized dimensions in trustworthy artificial intelligence (AI). Various explainable AI methods have been introduced to improve…
Integrating Knowledge Distillation Methods: A Sequential Multi-Stage Framework
Yinxi Tian, Changwu Huang, Ke Tang +1
Knowledge distillation (KD) transfers knowledge from large teacher models to compact student models, enabling efficient deployment on resource constrained devices. While diverse KD…
On the Fairness of Privacy Protection: Measuring and Mitigating the Disparity of Group Privacy Risks for Differentially Private Machine Learning
Zhi Yang, Changwu Huang, Ke Tang +1
While significant progress has been made in conventional fairness-aware machine learning (ML) and differentially private ML (DPML), the fairness of privacy protection across groups…
Procedural Fairness and Its Relationship with Distributive Fairness in Machine Learning
Ziming Wang, Changwu Huang, Ke Tang +1
Fairness in machine learning (ML) has garnered significant attention in recent years. While existing research has predominantly focused on the distributive fairness of ML models, t…