Publications

[Google Scholar] [DBLP] [ORCID]

Preprint

• [Preprint] Causally Fair Node Classification on Non-IID Graph Data
  Yucong Dai, Lu Zhang, Yaowei Hu, Susan Gauch, Yongkai Wu
  [arXiv]

• [Preprint] FairSAM: Fair Classification on Corrupted Data Through Sharpness-Aware Minimization
  Yucong Dai, Jie Ji, Xiaolong Ma, Yongkai Wu
  [arXiv]

• [Preprint] Fair Diagnosis: Leveraging Causal Modeling to Mitigate Medical Bias
  Bowei Tian, Yexiao He, Meng Liu, Yucong Dai, Ziyao Wang, Shwai He, Guoheng Sun, Zheyu Shen, Wanghao Ye, Yongkai Wu, Ang Li
  [arXiv]

• [Preprint] Coupling Fairness and Pruning in a Single Run: a Bi-level Optimization Perspective
  Yucong Dai, Gen Li, Feng Luo, Xiaolong Ma, Yongkai Wu
  [arXiv]

• [Preprint] Algorithmic Recourse for Anomaly Detection in Multivariate Time Series
  Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
  [arXiv]

2025

• [ICLR '25] Towards Counterfactual Fairness Through Auxiliary Variables
  Bowei Tian, Ziyao Wang, Shwai He, Wanghao Ye, Guoheng Sun, Yucong Dai, Yongkai Wu, Ang Li
  [arXiv]

• [AAAI '25] Fair Graph U-Net: A Fair Graph Learning Framework Integrating Group and Individual Awareness
  Zichong Wang, Zhibo Chu, Thang Viet Doan, Shaowei Wang, Yongkai Wu, Vasile Palade, Wenbin Zhang [DOI] [DBLP]

2024

• [NeurIPS '24] SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning
  Yexiao He, Ziyao Wang, Zheyu Shen, Guoheng Sun, Yucong Dai, Yongkai Wu, Hongyi Wang, Ang Li
  [arXiv] [Paper] [DBLP]

• [TMLR '24] From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling
  Aneesh Komanduri, Xintao Wu, Yongkai Wu, Feng Chen
  [arXiv] [Paper] [DBLP]

• [IJCAI '24] Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms
  Aneesh Komanduri, Yongkai Wu, Feng Chen, Xintao Wu
  [arXiv] [DOI] [DBLP]

• [IJCNN '24] Fair Weak-Supervised Learning: A Multiple-Instance Learning Approach
  Yucong Dai, Xiangyu Jiang, Yaowei Hu, Lu Zhang, Yongkai Wu
  [Paper] [DOI] [DBLP]

• [IJCNN '24] Achieving Fairness Through Constrained Recourse
  Shuang Wang, Yongkai Wu
  [Paper] [DOI] [DBLP]

• [IJCNN '24] Achieving Equalized Explainability Through Data Reconstruction
  Shuang Wang, Yongkai Wu
  [Paper] [DOI] [DBLP]

• [MLSys'24] SiDA: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Model
  Zhixu Du, Shiyu Li, Yuhao Wu, Xiangyu Jiang, Jingwei Sun, Qilin Zheng, Yongkai Wu, Ang Li, Hai "Helen" Li, Yiran Chen
  [arXiv] [DOI] [DBLP]

• [SDM '24] Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach
  Karuna Bhaila, Wen Huang, Yongkai Wu, Xintao Wu
  [arXiv] [DOI] [DBLP]

• [AAAI '24] Long-term Fair Decision Making Through Deep Generative Models
  Yaowei Hu, Yongkai Wu, Lu Zhang
  [arXiv] [DOI] [DBLP] [Poster] [Code]

2023

• [Book Chapter] Fair Machine Learning Through the Lens of Causality
  Yongkai Wu, Lu Zhang, Xintao Wu
  [DOI]

• [NeurIPS GLFrontiers '23] Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach
  Karuna Bhaila, Wen Huang, Yongkai Wu, Xintao Wu
  [arXiv]

• [CRL@NeurIPS '23] Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms
  Aneesh Komanduri, Yongkai Wu, Feng Chen, Xintao Wu
  [arXiv]

• [CIKM '23] On Root Cause Localization and Anomaly Mitigation through Causal Inference
  Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
  [arXiv] [DOI] [DBLP]

• [KDD-EAI '23] Long-term Fair Decision Making Through Deep Generative Models
  Yaowei Hu, Yongkai Wu, Lu Zhang

• [IJCNN '23] Fair Selection Through Kernel Density Estimation
  Xiangyu Jiang, Yucong Dai, Yongkai Wu
  [DOI] [DBLP]

• [IJCNN '23] Neural Time-Invariant Causal Discovery from Time Series Data
  Saima Absar, Yongkai Wu, Lu Zhang
  [DOI] [DBLP]

• [PAKDD '23] Achieving Counterfactual Fairness for Anomaly Detection
  Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
  [arXiv] [DBLP] [DOI] [Code]

2022

• [Big Data '22] SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge
  Aneesh Komanduri, Yongkai Wu, Wen Huang, Feng Chen, Xintao Wu
  [DOI] [DBLP]

• [Big Data '22] Fair Collective Classification in Networked Data
  Karuna Bhaila, Yongkai Wu, Xintao Wu
  [DOI] [DBLP]

2021

• [AAAI '21] A Generative Adversarial Framework for Bounding Confounded Causal Effects
  Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu
  [Paper] [Code] [DBLP] [Proceeding]

2020

• [NeurIPS '20] Fair Multiple Decision Making through Soft Interventions
  Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu
  [Paper] [Code] [DBLP] [Proceeding]

• [SBP-BRiMS '20] Multi-cause Discrimination Analysis Using Potential Outcomes
  Wen Huang, Yongkai Wu, Xintao Wu
  [DOI] [DBLP]

• [Dissertation] Achieving Causal Fairness in Machine Learning
  Yongkai Wu
  [Paper] [Proquest]

• [FATES '20] Fairness through Equality of Effort
  Wen Huang, Yongkai Wu, Lu Zhang, Xintao Wu
  [arXiv] [Paper] [DOI] [DBLP] [Proceeding]

2019

• [NeurIPS '19] PC-Fairness: A Unified Framework for Measuring Causality-based Fairness
  Yongkai Wu, Lu Zhang, Xintao Wu, Hanghang Tong
  [arXiv] [Paper] [Code] [Poster] [DBLP] [Proceeding]

• [IJCAI '19] Counterfactual Fairness: Unidentification, Bound and Algorithm
  Yongkai Wu, Lu Zhang, Xintao Wu
  [Paper] [Code] [DOI] [DBLP]

• [IJCAI '19] Achieving Causal Fairness through Generative Adversarial Networks
  Depeng Xu, Yongkai Wu, Shuhan Yuan, Lu Zhang, Xintao Wu
  [Paper] [DOI] [DBLP]

• [WWW '19] On Convexity and Bounds of Fairness-Aware Classification
  Yongkai Wu, Lu Zhang, Xintao Wu
  [arXiv] [Paper] [Code] [DOI] [DBLP] [Proceeding]

2018

• [TKDE '18] Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms
  Lu Zhang, Yongkai Wu, Xintao Wu
  [Code] [DOI] [DBLP]

• [KDD '18] On Discrimination Discovery and Removal in Ranked Data Using Causal Graph
  Yongkai Wu, Lu Zhang, Xintao Wu
  [arXiv] [Paper] [Code] [DOI] [DBLP]

• [IJCAI '18] Achieving Non-Discrimination in Prediction
  Lu Zhang, Yongkai Wu, Xintao Wu
  [arXiv] [Paper] [Code] [DOI] [DBLP]

2017

• [IJCAI '17] A Causal Framework for Discovering and Removing Direct and Indirect Discrimination
  Lu Zhang, Yongkai Wu, Xintao Wu
  [arXiv] [Paper] [Code] [DOI] [DBLP]

• [KDD '17] Achieving Non-Discrimination in Data Release
  Lu Zhang, Yongkai Wu, Xintao Wu
  [arXiv] [Paper] [Code] [DOI] [DBLP]

• [PAC '17] DPWeka: Achieving Differential Privacy in WEKA
  Srinidhi Katla, Depeng Xu, Yongkai Wu, Qiuping Pan, Xintao Wu
  [Code] [DOI] [DBLP]

2016

• [DSAA '16] Using Loglinear Model for Discrimination Discovery and Prevention
  Yongkai Wu, Xintao Wu
  [Paper] [DOI] [DBLP]

• [IJCAI '16] Situation Testing-Based Discrimination Discovery: A Causal Inference Approach
  Lu Zhang, Yongkai Wu, Xintao Wu
  [Paper] [DBLP] [Proceeding]

• [SBP-BRiMS '16] On Discrimination Discovery Using Causal Networks
  Lu Zhang, Yongkai Wu, Xintao Wu
  [Paper] [DOI] [DBLP]