1 citations · 1 across the 7 of their papers we have counts for
7 papers
LambdaRankIC: Directly Optimizing Rank IC for Financial Prediction
Yan Lin, Yihong Su, Yi Yang
In financial predictions, the performance of machine learning models is often assessed by Rank IC, which is the Spearman rank correlation between the model predictions and the real…
Fairness May Backfire: When Leveling-Down Occurs in Fair Machine Learning
Yi Yang, Xiangyu Chang, Pei-yu Chen
As machine learning (ML) systems increasingly shape access to credit, jobs, and other opportunities, the fairness of algorithmic decisions has become a central concern. Yet it rema…
Beyond Uniform Deletion: A Data Value-Weighted Framework for Certified Machine Unlearning
Lisong He, Yi Yang, Xiangyu Chang
As the right to be forgotten becomes legislated worldwide, machine unlearning mechanisms have emerged to efficiently update models for data deletion and enhance user privacy protec…
Bayes-Optimal Fair Classification with Multiple Sensitive Features
Yi Yang, Yinghui Huang, Xiangyu Chang
Existing theoretical work on Bayes-optimal fair classifiers usually considers a single (binary) sensitive feature. In practice, individuals are often defined by multiple sensitive…
PPFL: A Personalized Federated Learning Framework for Heterogeneous Population
Hao Di, Yi Yang, Haishan Ye +1
Personalization aims to characterize individual preferences and is widely applied across many fields. However, conventional personalized methods operate in a centralized manner, po…
Toward a Fairness-Aware Scoring System for Algorithmic Decision-Making
Yi Yang, Ying Wu, Mei Li +2
Scoring systems, as a type of predictive model, have significant advantages in interpretability and transparency and facilitate quick decision-making. As such, scoring systems have…
Angle-Based Cost-Sensitive Multicategory Classification
Yi Yang, Yuxuan Guo, Xiangyu Chang
Many real-world classification problems come with costs which can vary for different types of misclassification. It is thus important to develop cost-sensitive classifiers which mi…