Using Loglinear Model for Discrimination Discovery and Prevention. - Yongkai Wu; Xintao Wu (DSAA, 2016)

Situation Testing-Based Discrimination Discovery: A Causal Inference Approach. - Lu Zhang 021; Yongkai Wu; Xintao Wu (IJCAI, 2016)

On Discrimination Discovery Using Causal Networks. - Lu Zhang 021; Yongkai Wu; Xintao Wu (SBP-BRiMS, 2016)

A Causal Framework for Discovering and Removing Direct and Indirect Discrimination. - Lu Zhang 021; Yongkai Wu; Xintao Wu (IJCAI, 2017)

Achieving Non-Discrimination in Data Release. - Lu Zhang 021; Yongkai Wu; Xintao Wu (KDD, 2017)

DPWeka: Achieving Differential Privacy in WEKA. - Srinidhi Katla; Depeng Xu 001; Yongkai Wu; Qiuping Pan; Xintao Wu (PAC, 2017)

Achieving Non-Discrimination in Prediction. - Lu Zhang 021; Yongkai Wu; Xintao Wu (IJCAI, 2018)

On Discrimination Discovery and Removal in Ranked Data using Causal Graph. - Yongkai Wu; Lu Zhang 021; Xintao Wu (KDD, 2018)

Fairness-aware Classification: Criterion, Convexity, and Bounds. - Yongkai Wu; Lu Zhang 021; Xintao Wu (CoRR, 2018)

Counterfactual Fairness: Unidentification, Bound and Algorithm. - Yongkai Wu; Lu Zhang 021; Xintao Wu (IJCAI, 2019)

Achieving Causal Fairness through Generative Adversarial Networks. - Depeng Xu 001; Yongkai Wu; Shuhan Yuan; Lu Zhang 021; Xintao Wu (IJCAI, 2019)

PC-Fairness: A Unified Framework for Measuring Causality-based Fairness. - Yongkai Wu; Lu Zhang 021; Xintao Wu; Hanghang Tong (NeurIPS, 2019)

On Convexity and Bounds of Fairness-aware Classification. - Yongkai Wu; Lu Zhang 021; Xintao Wu (WWW, 2019)

Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms. - Lu Zhang 021; Yongkai Wu; Xintao Wu (IEEE Trans. Knowl. Data Eng., 2019)

Fair Multiple Decision Making Through Soft Interventions. - Yaowei Hu 001; Yongkai Wu; Lu Zhang 021; Xintao Wu (NeurIPS, 2020)

Multi-cause Discrimination Analysis Using Potential Outcomes. - Wen Huang 003; Yongkai Wu; Xintao Wu (SBP-BRiMS, 2020)

Fairness through Equality of Effort. - Wen Huang 003; Yongkai Wu; Lu Zhang 021; Xintao Wu (WWW (Companion Volume), 2020)

A Generative Adversarial Framework for Bounding Confounded Causal Effects. - Yaowei Hu 001; Yongkai Wu; Lu Zhang 021; Xintao Wu (AAAI, 2021)

Fair Collective Classification in Networked Data. - Karuna Bhaila; Yongkai Wu; Xintao Wu (IEEE Big Data, 2022)

SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge. - Aneesh Komanduri; Yongkai Wu; Wen Huang 003; Feng Chen 001; Xintao Wu (IEEE Big Data, 2022)

On Interpretable Anomaly Detection Using Causal Algorithmic Recourse. - Xiao Han 008; Lu Zhang 021; Yongkai Wu; Shuhan Yuan (CoRR, 2022)

On Root Cause Localization and Anomaly Mitigation through Causal Inference. - Xiao Han 008; Lu Zhang 021; Yongkai Wu; Shuhan Yuan (CIKM, 2023)

Artificial Intelligence for Climate Smart Forestry: A Forward Looking Vision. - Feng Luo 001; Ling Liu; G. Geoff Wang; Vijay Kumar 001; Mark S. Ashton; Jacob D. Abernethy; Fatemeh Afghah; Matthew H. E. M. Browning; David Coyle; Philip M. Dames; Tom O'Halloran; James Hays; et al. (CogMI, 2023)

Neural Time-Invariant Causal Discovery from Time Series Data. - Saima Absar; Yongkai Wu; Lu Zhang 021 (IJCNN, 2023)

Fair Selection through Kernel Density Estimation. - Xiangyu Jiang; Yucong Dai; Yongkai Wu (IJCNN, 2023)

Achieving Counterfactual Fairness for Anomaly Detection. - Xiao Han 008; Lu Zhang 021; Yongkai Wu; Shuhan Yuan (PAKDD (1), 2023)

Algorithmic Recourse for Anomaly Detection in Multivariate Time Series. - Xiao Han 008; Lu Zhang 021; Yongkai Wu; Shuhan Yuan (CoRR, 2023)

Coupling Fairness and Pruning in a Single Run: a Bi-level Optimization Perspective. - Yucong Dai; Gen Li 012; Feng Luo 001; Xiaolong Ma; Yongkai Wu (CoRR, 2023)

Long-Term Fair Decision Making through Deep Generative Models. - Yaowei Hu 001; Yongkai Wu; Lu Zhang 021 (AAAI, 2024)

Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms. - Aneesh Komanduri; Yongkai Wu; Feng Chen 001; Xintao Wu (IJCAI, 2024)

Fair Weak-Supervised Learning: A Multiple-Instance Learning Approach. - Yucong Dai; Xiangyu Jiang; Yaowei Hu 001; Lu Zhang 021; Yongkai Wu (IJCNN, 2024)

Achieving Equalized Explainability Through Data Reconstruction. - Shuang Wang 010; Yongkai Wu (IJCNN, 2024)

Achieving Fairness through Constrained Recourse. - Shuang Wang 010; Yongkai Wu (IJCNN, 2024)

SiDA: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models. - Zhixu Du; Shiyu Li 001; Yuhao Wu; Xiangyu Jiang; Jingwei Sun 002; Qilin Zheng; Yongkai Wu; Ang Li 005; Hai Li 001; Yiran Chen 001 (MLSys, 2024)

SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning. - Yexiao He; Ziyao Wang; Zheyu Shen; Guoheng Sun; Yucong Dai; Yongkai Wu; Hongyi Wang 001; Ang Li 005 (NeurIPS, 2024)

Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach. - Karuna Bhaila; Wen Huang 003; Yongkai Wu; Xintao Wu (SDM, 2024)

AI-Cybersecurity Education Through Designing AI-based Cyberharassment Detection Lab. - Ebuka Okpala; Nishant Vishwamitra; Keyan Guo; Song Liao; Long Cheng 005; Hongxin Hu; Yongkai Wu; Xiaohong Yuan; Jeannette Wade; Sajad Khorsandroo (CoRR, 2024)

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 005 (CoRR, 2024)

Towards counterfactual fairness thorough auxiliary variables. - Bowei Tian; Ziyao Wang; Shwai He; Wanghao Ye; Guoheng Sun; Yucong Dai; Yongkai Wu; Ang Li 005 (CoRR, 2024)

From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling. - Aneesh Komanduri; Xintao Wu; Yongkai Wu; Feng Chen 001 (Trans. Mach. Learn. Res., 2024)

Fair Graph U-Net: A Fair Graph Learning Framework Integrating Group and Individual Awareness. - Zichong Wang; Zhibo Chu; Thang Viet Doan; Shaowei Wang 002; Yongkai Wu; Vasile Palade; Wenbin Zhang 002 (AAAI, 2025)

Towards counterfactual fairness through auxiliary variables. - Bowei Tian; Ziyao Wang; Shwai He; Wanghao Ye; Guoheng Sun; Yucong Dai; Yongkai Wu; Ang Li 005 (ICLR, 2025)

FairSAM: Fair Classification on Corrupted Data Through Sharpness-Aware Minimization. - Yucong Dai; Jie Ji; Xiaolong Ma; Yongkai Wu (CoRR, 2025)

Causally Fair Node Classification on Non-IID Graph Data. - Yucong Dai; Lu Zhang 021; Yaowei Hu 001; Susan Gauch; Yongkai Wu (CoRR, 2025)