4 citations · 5 across the 5 of their papers we have counts for
5 papers
CASE: Causal Alignment and Structural Enforcement for Improving Chain-of-Thought Faithfulness
Ziming Wang, Yinghua Yao, Changwu Huang +2
Chain-of-thought (CoT) reasoning is widely used to improve both the performance and interpretability of large language models (LLMs), yet the generated reasoning may not faithfully…
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…
Procedural Fairness in Machine Learning
Ziming Wang, Changwu Huang, Ke Tang +1
Fairness in machine learning (ML) has garnered significant attention. However, current research has mainly concentrated on the distributive fairness of ML models, with limited focu…
Anti-Matthew FL: Bridging the Performance Gap in Federated Learning to Counteract the Matthew Effect
Jiashi Gao, Xin Yao, Xuetao Wei
Federated learning (FL) stands as a paradigmatic approach that facilitates model training across heterogeneous and diverse datasets originating from various data providers. However…