77 citations · 175 across the 14 of their papers we have counts for
5 papers · 1 filter
Fair Few-shot Learning with Auxiliary Sets
Song Wang, Jing Ma, Lu Cheng +1
Recently, there has been a growing interest in developing machine learning (ML) models that can promote fairness, i.e., eliminating biased predictions towards certain populations (…
A Look into Causal Effects under Entangled Treatment in Graphs: Investigating the Impact of Contact on MRSA Infection
Jing Ma, Chen Chen, Anil Vullikanti +4
Methicillin-resistant Staphylococcus aureus (MRSA) is a type of bacteria resistant to certain antibiotics, making it difficult to prevent MRSA infections. Among decades of efforts…
Learning for Counterfactual Fairness from Observational Data
Jing Ma, Ruocheng Guo, Aidong Zhang +1
Fairness-aware machine learning has attracted a surge of attention in many domains, such as online advertising, personalized recommendation, and social media analysis in web applic…
Path-Specific Counterfactual Fairness for Recommender Systems
Yaochen Zhu, Jing Ma, Liang Wu +3
Recommender systems (RSs) have become an indispensable part of online platforms. With the growing concerns of algorithmic fairness, RSs are not only expected to deliver high-qualit…
Causal Inference in Recommender Systems: A Survey of Strategies for Bias Mitigation, Explanation, and Generalization
Yaochen Zhu, Jing Ma, Jundong Li
In the era of information overload, recommender systems (RSs) have become an indispensable part of online service platforms. Traditional RSs estimate user interests and predict the…