Publications

Preprints

2025

  1. Preprint
    Causally Fair Node Classification on Non-IID Graph Data
    Yucong Dai, Lu Zhang, Yaowei Hu, Susan Gauch, and Yongkai Wu
    May 2025
    DOI HTML
  2. Preprint
    FairSAM: Fair Classification on Corrupted Data Through Sharpness-Aware Minimization
    Yucong Dai, Jie Ji, Xiaolong Ma, and Yongkai Wu
    Mar 2025
    DOI HTML

2024

  1. 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, and Ang Li
    Dec 2024
    DOI HTML

2023

  1. Preprint
    Coupling Fairness and Pruning in a Single Run: A Bi-level Optimization Perspective
    Yucong Dai, Gen Li, Feng Luo, Xiaolong Ma, and Yongkai Wu
    Dec 2023
    DOI HTML
  2. Preprint
    Algorithmic Recourse for Anomaly Detection in Multivariate Time Series
    Xiao Han, Lu Zhang, Yongkai Wu, and Shuhan Yuan
    Sep 2023
    DOI HTML

Peer-reviewed Articles

2025

  1. ICDM Demo
    FairAgent: Democratizing Fairness-Aware Machine Learning with LLM-Powered Agents
    Yucong Dai, Lu Zhang, Feng Luo, Mashrur Chowdhury, and Yongkai Wu
    Oct 2025
    DOI HTML
  2. ICLR
    Towards Counterfactual Fairness through Auxiliary Variables
    Bowei Tian, Ziyao Wang, Shwai He, Wanghao Ye, Guoheng Sun, Yucong Dai, Yongkai Wu, and Ang Li
    In International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025, Apr 2025
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  3. AAAI
    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, and Wenbin Zhang
    In AAAI Conference on Artificial Intelligence, AAAI 2025, February 25 - March 4, 2025, Philadelphia, PA, USA, Feb 2025
    DOI HTML

2024

  1. NeurIPS
    SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning
    Yexiao He, Ziyao Wang, Zheyu Shen, Guoheng Sun, Yucong Dai, Yongkai Wu, Hongyi Wang, and Ang Li
    In Neural Information Processing Systems, NeurIPS 2024, December 2-8, 2024, Vancouver, BC, Canada, Nov 2024
    arXiv HTML
  2. IJCAI
    Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms
    Aneesh Komanduri, Yongkai Wu, Feng Chen, and Xintao Wu
    In International Joint Conference on Artificial Intelligence, IJCAI 2024, Jeju, South Korea, August 3-9, 2024, Aug 2024
    arXiv HTML
  3. IJCNN
    Fair Weak-Supervised Learning: A Multiple-Instance Learning Approach
    Yucong Dai, Xiangyu Jiang, Yaowei Hu, Lu Zhang, and Yongkai Wu
    In International Joint Conference on Neural Networks, IJCNN 2024, Yokohama, Japan, June 30 - July 5, 2024, Jun 2024
    DOI HTML
  4. IJCNN
    Achieving Equalized Explainability through Data Reconstruction
    Shuang Wang and Yongkai Wu
    In International Joint Conference on Neural Networks, IJCNN 2024, Yokohama, Japan, June 30 - July 5, 2024, Jun 2024
    DOI HTML
  5. IJCNN
    Achieving Fairness through Constrained Recourse
    Shuang Wang and Yongkai Wu
    In International Joint Conference on Neural Networks, IJCNN 2024, Yokohama, Japan, June 30 - July 5, 2024, Jun 2024
    DOI
  6. MLSys
    SiDA: Sparsity-inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models
    Zhixu Du, Shiyu Li, Yuhao Wu, Xiangyu Jiang, Jingwei Sun, Qilin Zheng, Yongkai Wu, Ang Li, Hai Li, and Yiran Chen
    In Machine Learning and Systems, MLSys 2024, Santa Clara, CA, USA, May 13-16, 2024, May 2024
    arXiv HTML
  7. TMLR
    From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling
    Aneesh Komanduri, Xintao Wu, Yongkai Wu, and Feng Chen
    Transactions on Machine Learning Research, May 2024
    arXiv HTML
  8. SDM
    Local Differential Privacy in Graph Neural Networks: A Reconstruction Approach
    Karuna Bhaila, Wen Huang, Yongkai Wu, and Xintao Wu
    In SIAM International Conference on Data Mining, SDM 2024, Houston, TX, USA, April 18-20, 2024, Apr 2024
    DOI arXiv HTML
  9. AAAI
    Long-Term Fair Decision Making through Deep Generative Models
    Yaowei Hu, Yongkai Wu, and Lu Zhang
    In AAAI Conference on Artificial Intelligence, AAAI 2024, February 20-27, 2024, Vancouver, Canada, Feb 2024
    DOI arXiv HTML Code Poster

2023

  1. CogMI
    Artificial Intelligence for Climate Smart Forestry: A Forward Looking Vision
    Feng Luo, Ling Liu, G. Geoff Wang, Vijay Kumar, Mark S. Ashton, Jacob D. Abernethy, Fatemeh Afghah, Matthew H. E. M. Browning, David Coyle, Philip M. Dames, Tom O’Halloran, James Hays, Patrick Hiesl, Chenfanfu Jiang, Puskar Khanal, Venkat Narayan Krovi, Sara Kuebbing, Nianyi Li, JingJing Liang, Ninghao Liu, Steve McNulty, Christopher M. Oswalt, Neil Pederson, Demetri Terzopoulos, Christopher W. Woodall, Yongkai Wu, Jian Yang, Yin Yang, and Liang Zhao
    In IEEE International Conference on Cognitive Machine Intelligence, CogMI 2023, Atlanta, GA, USA, November 1-4, 2023, Nov 2023
    DOI HTML
  2. CIKM
    On Root Cause Localization and Anomaly Mitigation through Causal Inference
    Xiao Han, Lu Zhang, Yongkai Wu, and Shuhan Yuan
    In ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023, Oct 2023
    DOI arXiv HTML
  3. IJCNN
    Neural Time-Invariant Causal Discovery from Time Series Data
    Saima Absar, Yongkai Wu, and Lu Zhang
    In International Joint Conference on Neural Networks, IJCNN 2023, Gold Coast, Australia, June 18-23, 2023, Jun 2023
    DOI HTML
  4. IJCNN
    Fair Selection through Kernel Density Estimation
    Xiangyu Jiang, Yucong Dai, and Yongkai Wu
    In International Joint Conference on Neural Networks, IJCNN 2023, Gold Coast, Australia, June 18-23, 2023, Jun 2023
    DOI HTML
  5. PAKDD
    Achieving Counterfactual Fairness for Anomaly Detection
    Xiao Han, Lu Zhang, Yongkai Wu, and Shuhan Yuan
    In Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25-28, 2023, Proceedings, Part I, May 2023
    DOI arXiv HTML Code

2022

  1. Big Data
    Fair Collective Classification in Networked Data
    Karuna Bhaila, Yongkai Wu, and Xintao Wu
    In IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, December 17-20, 2022, Dec 2022
    DOI HTML
  2. Big Data
    SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge
    Aneesh Komanduri, Yongkai Wu, Wen Huang, Feng Chen, and Xintao Wu
    In IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, December 17-20, 2022, Dec 2022
    DOI HTML

2021

  1. AAAI
    A Generative Adversarial Framework for Bounding Confounded Causal Effects
    Yaowei Hu, Yongkai Wu, Lu Zhang, and Xintao Wu
    In AAAI Conference on Artificial Intelligence, AAAI 2021, February 2-9, 2021, Virtual Event, Feb 2021
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2020

  1. NeurIPS
    Fair Multiple Decision Making through Soft Interventions
    Yaowei Hu, Yongkai Wu, Lu Zhang, and Xintao Wu
    In Annual Conference on Neural Information Processing Systems, NeurIPS 2020, December 6-12, 2020, Virtual, Dec 2020
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  2. SBP-BRiMS
    Multi-Cause Discrimination Analysis Using Potential Outcomes
    Wen Huang, Yongkai Wu, and Xintao Wu
    In International Conference on Social, Cultural, and Behavioral Modeling, SBP-BRiMS 2020, Washington, DC, USA, October 18-21, 2020, Oct 2020
    DOI HTML
  3. FATES
    Fairness through Equality of Effort
    Wen Huang, Yongkai Wu, Lu Zhang, and Xintao Wu
    In Companion of the 2020 Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, Apr 2020
    DOI arXiv HTML

2019

  1. NeurIPS
    PC-Fairness: A Unified Framework for Measuring Causality-Based Fairness
    Yongkai Wu, Lu Zhang, Xintao Wu, and Hanghang Tong
    In Annual Conference on Neural Information Processing Systems, NeurIPS 2019, 8-14 December 2019, Vancouver, BC, Canada, Dec 2019
    arXiv HTML
  2. TKDE
    Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms
    Lu Zhang, Yongkai Wu, and Xintao Wu
    IEEE Transactions on Knowledge and Data Engineering, Nov 2019
    DOI HTML
  3. IJCAI
    Counterfactual Fairness: Unidentification, Bound and Algorithm
    Yongkai Wu, Lu Zhang, and Xintao Wu
    In International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, Aug 2019
    DOI HTML
  4. IJCAI
    Achieving Causal Fairness through Generative Adversarial Networks
    Depeng Xu, Yongkai Wu, Shuhan Yuan, Lu Zhang, and Xintao Wu
    In International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, Aug 2019
    DOI HTML
  5. WWW
    On Convexity and Bounds of Fairness-Aware Classification
    Yongkai Wu, Lu Zhang, and Xintao Wu
    In World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019, May 2019
    DOI arXiv HTML

2018

  1. KDD
    On Discrimination Discovery and Removal in Ranked Data Using Causal Graph
    Yongkai Wu, Lu Zhang, and Xintao Wu
    In ACM International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19-23, 2018, Aug 2018
    DOI arXiv HTML
  2. IJCAI
    Achieving Non-Discrimination in Prediction
    Lu Zhang, Yongkai Wu, and Xintao Wu
    In International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden, Jul 2018
    DOI arXiv HTML

2017

  1. IJCAI
    A Causal Framework for Discovering and Removing Direct and Indirect Discrimination
    Lu Zhang, Yongkai Wu, and Xintao Wu
    In International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017, Aug 2017
    DOI arXiv HTML
  2. KDD
    Achieving Non-Discrimination in Data Release
    Lu Zhang, Yongkai Wu, and Xintao Wu
    In ACM International Conference on Knowledge Discovery and Data Mining, KDD 2017, Halifax, NS, Canada, August 13 - 17, 2017, Aug 2017
    DOI arXiv HTML
  3. PAC
    DPWeka: Achieving Differential Privacy in WEKA
    Srinidhi Katla, Depeng Xu, Yongkai Wu, Qiuping Pan, and Xintao Wu
    In IEEE Symposium on Privacy-Aware Computing, PAC 2017, Washington, DC, USA, August 1-4, 2017, Aug 2017
    DOI HTML Code

2016

  1. DSAA
    Using Loglinear Model for Discrimination Discovery and Prevention
    Yongkai Wu and Xintao Wu
    In IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016, Montreal, QC, Canada, October 17-19, 2016, Oct 2016
    DOI HTML
  2. IJCAI
    Situation Testing-Based Discrimination Discovery: A Causal Inference Approach
    Lu Zhang, Yongkai Wu, and Xintao Wu
    In International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016, Jul 2016
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  3. SBP-BRiMS
    On Discrimination Discovery Using Causal Networks
    Lu Zhang, Yongkai Wu, and Xintao Wu
    In International Conference on Social, Cultural, and Behavioral Modeling, SBP-BRiMS 2016, Washington, DC, USA, June 28 - July 1, 2016, Jun 2016
    DOI HTML

Dissertation

2020

  1. Dissertation
    Achieving Causal Fairness in Machine Learning
    Yongkai Wu
    University of Arkansas, May 2020
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