Yongkai Wu - RankMe

60 papers A* 16A 2B 8Journal 26Unranked 8

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Algorithmic Recourse in Abnormal Multivariate Time Series. Trans. Mach. Learn. Res. Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
2025	J	`jnl`	Causally Fair Node Classification on Non-IID Graph Data. CoRR Yucong Dai, Lu Zhang, Yaowei Hu, Susan Gauch, Yongkai Wu
2025	A*	`conf`	Fair Graph U-Net: A Fair Graph Learning Framework Integrating Group and Individual Awareness. AAAI Zichong Wang, Zhibo Chu, Thang Viet Doan, Shaowei Wang, Yongkai Wu, Vasile Palade, Wenbin Zhang
2025	—	`conf`	FairAgent: Democratizing Fairness-Aware Machine Learning with LLM-Powered Agents. ICDM (Workshops) Yucong Dai, Lu Zhang, Feng Luo, Mashrur Chowdhury, Yongkai Wu
2025	J	`jnl`	FairAgent: Democratizing Fairness-Aware Machine Learning with LLM-Powered Agents. CoRR Yucong Dai, Lu Zhang, Feng Luo, Mashrur Chowdhury, Yongkai Wu
2025	J	`jnl`	FairSAM: Fair Classification on Corrupted Data Through Sharpness-Aware Minimization. CoRR Yucong Dai, Jie Ji, Xiaolong Ma, Yongkai Wu
2025	A*	`conf`	Towards counterfactual fairness through auxiliary variables. ICLR Bowei Tian, Ziyao Wang, Shwai He, Wanghao Ye, Guoheng Sun, Yucong Dai, Yongkai Wu, Ang Li
2024	J	`jnl`	AI-Cybersecurity Education Through Designing AI-based Cyberharassment Detection Lab. CoRR Ebuka Okpala, Nishant Vishwamitra, Keyan Guo, Song Liao, Long Cheng, Hongxin Hu, Yongkai Wu, Xiaohong Yuan, Jeannette Wade, Sajad Khorsandroo
2024	B	`conf`	Achieving Equalized Explainability Through Data Reconstruction. IJCNN Shuang Wang, Yongkai Wu
2024	B	`conf`	Achieving Fairness through Constrained Recourse. IJCNN Shuang Wang, Yongkai Wu
2024	J	`jnl`	Fair Diagnosis: Leveraging Causal Modeling to Mitigate Medical Bias. CoRR Bowei Tian, Yexiao He, Meng Liu, Yucong Dai, Ziyao Wang, Shwai He, Guoheng Sun, Zheyu Shen, Wanghao Ye, Yongkai Wu, Ang Li
2024	B	`conf`	Fair Weak-Supervised Learning: A Multiple-Instance Learning Approach. IJCNN Yucong Dai, Xiangyu Jiang, Yaowei Hu, Lu Zhang, Yongkai Wu
2024	J	`jnl`	From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling. Trans. Mach. Learn. Res. Aneesh Komanduri, Xintao Wu, Yongkai Wu, Feng Chen
2024	A*	`conf`	Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms. IJCAI Aneesh Komanduri, Yongkai Wu, Feng Chen, Xintao Wu
2024	A	`conf`	Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach. SDM Karuna Bhaila, Wen Huang, Yongkai Wu, Xintao Wu
2024	A*	`conf`	Long-Term Fair Decision Making through Deep Generative Models. AAAI Yaowei Hu, Yongkai Wu, Lu Zhang
2024	J	`jnl`	Long-Term Fair Decision Making through Deep Generative Models. CoRR Yaowei Hu, Yongkai Wu, Lu Zhang
2024	A*	`conf`	SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning. NeurIPS Yexiao He, Ziyao Wang, Zheyu Shen, Guoheng Sun, Yucong Dai, Yongkai Wu, Hongyi Wang, Ang Li
2024	J	`jnl`	SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning. CoRR Yexiao He, Ziyao Wang, Zheyu Shen, Guoheng Sun, Yucong Dai, Yongkai Wu, Hongyi Wang, Ang Li
2024	—	`conf`	SiDA: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models. MLSys Zhixu Du, Shiyu Li, Yuhao Wu, Xiangyu Jiang, Jingwei Sun, Qilin Zheng, Yongkai Wu, Ang Li, Hai Li, Yiran Chen
2024	J	`jnl`	Towards counterfactual fairness thorough auxiliary variables. CoRR Bowei Tian, Ziyao Wang, Shwai He, Wanghao Ye, Guoheng Sun, Yucong Dai, Yongkai Wu, Ang Li
2023	—	`conf`	Achieving Counterfactual Fairness for Anomaly Detection. PAKDD (1) Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
2023	J	`jnl`	Achieving Counterfactual Fairness for Anomaly Detection. CoRR Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
2023	J	`jnl`	Algorithmic Recourse for Anomaly Detection in Multivariate Time Series. CoRR Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
2023	—	`conf`	Artificial Intelligence for Climate Smart Forestry: A Forward Looking Vision. CogMI 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, Liang Zhao
2023	J	`jnl`	Coupling Fairness and Pruning in a Single Run: a Bi-level Optimization Perspective. CoRR Yucong Dai, Gen Li, Feng Luo, Xiaolong Ma, Yongkai Wu
2023	B	`conf`	Fair Selection through Kernel Density Estimation. IJCNN Xiangyu Jiang, Yucong Dai, Yongkai Wu
2023	J	`jnl`	From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling. CoRR Aneesh Komanduri, Xintao Wu, Yongkai Wu, Feng Chen
2023	J	`jnl`	Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms. CoRR Aneesh Komanduri, Yongkai Wu, Feng Chen, Xintao Wu
2023	J	`jnl`	Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach. CoRR Karuna Bhaila, Wen Huang, Yongkai Wu, Xintao Wu
2023	B	`conf`	Neural Time-Invariant Causal Discovery from Time Series Data. IJCNN Saima Absar, Yongkai Wu, Lu Zhang
2023	A	`conf`	On Root Cause Localization and Anomaly Mitigation through Causal Inference. CIKM Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
2023	J	`jnl`	SiDA: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models. CoRR Zhixu Du, Shiyu Li, Yuhao Wu, Xiangyu Jiang, Jingwei Sun, Qilin Zheng, Yongkai Wu, Ang Li, Hai (Helen) Li, Yiran Chen
2022	B	`conf`	Fair Collective Classification in Networked Data. IEEE Big Data Karuna Bhaila, Yongkai Wu, Xintao Wu
2022	J	`jnl`	On Interpretable Anomaly Detection Using Causal Algorithmic Recourse. CoRR Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
2022	B	`conf`	SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge. IEEE Big Data Aneesh Komanduri, Yongkai Wu, Wen Huang, Feng Chen, Xintao Wu
2021	A*	`conf`	A Generative Adversarial Framework for Bounding Confounded Causal Effects. AAAI Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu
2020	A*	`conf`	Fair Multiple Decision Making Through Soft Interventions. NeurIPS Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu
2020	—	`conf`	Fairness through Equality of Effort. WWW (Companion Volume) Wen Huang, Yongkai Wu, Lu Zhang, Xintao Wu
2020	—	`conf`	Multi-cause Discrimination Analysis Using Potential Outcomes. SBP-BRiMS Wen Huang, Yongkai Wu, Xintao Wu
2019	A*	`conf`	Achieving Causal Fairness through Generative Adversarial Networks. IJCAI Depeng Xu, Yongkai Wu, Shuhan Yuan, Lu Zhang, Xintao Wu
2019	J	`jnl`	Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms. IEEE Trans. Knowl. Data Eng. Lu Zhang, Yongkai Wu, Xintao Wu
2019	A*	`conf`	Counterfactual Fairness: Unidentification, Bound and Algorithm. IJCAI Yongkai Wu, Lu Zhang, Xintao Wu
2019	J	`jnl`	Fairness through Equality of Effort. CoRR Wen Huang, Yongkai Wu, Lu Zhang, Xintao Wu
2019	A*	`conf`	On Convexity and Bounds of Fairness-aware Classification. WWW Yongkai Wu, Lu Zhang, Xintao Wu
2019	A*	`conf`	PC-Fairness: A Unified Framework for Measuring Causality-based Fairness. NeurIPS Yongkai Wu, Lu Zhang, Xintao Wu, Hanghang Tong
2019	J	`jnl`	PC-Fairness: A Unified Framework for Measuring Causality-based Fairness. CoRR Yongkai Wu, Lu Zhang, Xintao Wu, Hanghang Tong
2018	A*	`conf`	Achieving Non-Discrimination in Prediction. IJCAI Lu Zhang, Yongkai Wu, Xintao Wu
2018	J	`jnl`	Fairness-aware Classification: Criterion, Convexity, and Bounds. CoRR Yongkai Wu, Lu Zhang, Xintao Wu
2018	A*	`conf`	On Discrimination Discovery and Removal in Ranked Data using Causal Graph. KDD Yongkai Wu, Lu Zhang, Xintao Wu
2018	J	`jnl`	On Discrimination Discovery and Removal in Ranked Data using Causal Graph. CoRR Yongkai Wu, Lu Zhang, Xintao Wu
2017	A*	`conf`	A Causal Framework for Discovering and Removing Direct and Indirect Discrimination. IJCAI Lu Zhang, Yongkai Wu, Xintao Wu
2017	A*	`conf`	Achieving Non-Discrimination in Data Release. KDD Lu Zhang, Yongkai Wu, Xintao Wu
2017	J	`jnl`	Achieving non-discrimination in prediction. CoRR Lu Zhang, Yongkai Wu, Xintao Wu
2017	—	`conf`	DPWeka: Achieving Differential Privacy in WEKA. PAC Srinidhi Katla, Depeng Xu, Yongkai Wu, Qiuping Pan, Xintao Wu
2016	J	`jnl`	A causal framework for discovering and removing direct and indirect discrimination. CoRR Lu Zhang, Yongkai Wu, Xintao Wu
2016	J	`jnl`	Achieving non-discrimination in data release. CoRR Lu Zhang, Yongkai Wu, Xintao Wu
2016	—	`conf`	On Discrimination Discovery Using Causal Networks. SBP-BRiMS Lu Zhang, Yongkai Wu, Xintao Wu
2016	A*	`conf`	Situation Testing-Based Discrimination Discovery: A Causal Inference Approach. IJCAI Lu Zhang, Yongkai Wu, Xintao Wu
2016	B	`conf`	Using Loglinear Model for Discrimination Discovery and Prevention. DSAA Yongkai Wu, Xintao Wu