43 citations · 79 across the 11 of their papers we have counts for
11 papers
Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach
Karuna Bhaila, Wen Huang, Yongkai Wu +1
Graph Neural Networks have achieved tremendous success in modeling complex graph data in a variety of applications. However, there are limited studies investigating privacy protect…
Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms
Aneesh Komanduri, Yongkai Wu, Feng Chen +1
Learning disentangled causal representations is a challenging problem that has gained significant attention recently due to its implications for extracting meaningful information f…
Achieving Counterfactual Fairness for Anomaly Detection
Xiao Han, Lu Zhang, Yongkai Wu +1
Ensuring fairness in anomaly detection models has received much attention recently as many anomaly detection applications involve human beings. However, existing fair anomaly detec…
On Root Cause Localization and Anomaly Mitigation through Causal Inference
Xiao Han, Lu Zhang, Yongkai Wu +1
Due to a wide spectrum of applications in the real world, such as security, financial surveillance, and health risk, various deep anomaly detection models have been proposed and ac…
Fairness through Equality of Effort
Wen Huang, Yongkai Wu, Lu Zhang +1
Fair machine learning is receiving an increasing attention in machine learning fields. Researchers in fair learning have developed correlation or association-based measures such as…
PC-Fairness: A Unified Framework for Measuring Causality-based Fairness
Yongkai Wu, Lu Zhang, Xintao Wu +1
A recent trend of fair machine learning is to define fairness as causality-based notions which concern the causal connection between protected attributes and decisions. However, on…
Fairness-aware Classification: Criterion, Convexity, and Bounds
Yongkai Wu, Lu Zhang, Xintao Wu
Fairness-aware classification is receiving increasing attention in the machine learning fields. Recently research proposes to formulate the fairness-aware classification as constra…
On Discrimination Discovery and Removal in Ranked Data using Causal Graph
Yongkai Wu, Lu Zhang, Xintao Wu
Predictive models learned from historical data are widely used to help companies and organizations make decisions. However, they may digitally unfairly treat unwanted groups, raisi…
Achieving non-discrimination in prediction
Lu Zhang, Yongkai Wu, Xintao Wu
Discrimination-aware classification is receiving an increasing attention in data science fields. The pre-process methods for constructing a discrimination-free classifier first rem…
A causal framework for discovering and removing direct and indirect discrimination
Lu Zhang, Yongkai Wu, Xintao Wu
Anti-discrimination is an increasingly important task in data science. In this paper, we investigate the problem of discovering both direct and indirect discrimination from the his…
Achieving non-discrimination in data release
Lu Zhang, Yongkai Wu, Xintao Wu
Discrimination discovery and prevention/removal are increasingly important tasks in data mining. Discrimination discovery aims to unveil discriminatory practices on the protected a…