Yongkai Wu

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 46

Top Topics

Generative Model

Decision Making

Kernel Density Estimation

Causal Inference

Top Venues

Publications

Karuna Bhaila, Wen Huang, Yongkai Wu, Xintao Wu
Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach. SDM (2024)
Ebuka Okpala, Nishant Vishwamitra, Keyan Guo, Song Liao, Long Cheng, Hongxin Hu, Yongkai Wu, Xiaohong Yuan, Jeannette Wade, Sajad Khorsandroo
AI-Cybersecurity Education Through Designing AI-based Cyberharassment Detection Lab. CoRR (2024)
Yexiao He, Ziyao Wang, Zheyu Shen, Guoheng Sun, Yucong Dai, Yongkai Wu, Hongyi Wang, Ang Li
SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning. CoRR (2024)
Yaowei Hu, Yongkai Wu, Lu Zhang
Long-Term Fair Decision Making through Deep Generative Models. CoRR (2024)
Yaowei Hu, Yongkai Wu, Lu Zhang
Long-Term Fair Decision Making through Deep Generative Models. AAAI (2024)
Zhixu Du, Shiyu Li, Yuhao Wu, Xiangyu Jiang, Jingwei Sun, Qilin Zheng, Yongkai Wu, Ang Li, Hai Li, Yiran Chen
SiDA: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models. MLSys (2024)
Aneesh Komanduri, Xintao Wu, Yongkai Wu, Feng Chen
From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling. Trans. Mach. Learn. Res. 2024 (2024)
Xiangyu Jiang, Yucong Dai, Yongkai Wu
Fair Selection through Kernel Density Estimation. IJCNN (2023)
Yucong Dai, Gen Li, Feng Luo, Xiaolong Ma, Yongkai Wu
Coupling Fairness and Pruning in a Single Run: a Bi-level Optimization Perspective. CoRR (2023)
Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
Achieving Counterfactual Fairness for Anomaly Detection. CoRR (2023)
Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
On Root Cause Localization and Anomaly Mitigation through Causal Inference. CIKM (2023)
Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
Algorithmic Recourse for Anomaly Detection in Multivariate Time Series. CoRR (2023)
Karuna Bhaila, Wen Huang, Yongkai Wu, Xintao Wu
Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach. CoRR (2023)
Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
Achieving Counterfactual Fairness for Anomaly Detection. PAKDD (1) (2023)
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 Heisl, 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
Artificial Intelligence for Climate Smart Forestry: A Forward Looking Vision. CogMI (2023)
Aneesh Komanduri, Xintao Wu, Yongkai Wu, Feng Chen
From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling. CoRR (2023)
Saima Absar, Yongkai Wu, Lu Zhang
Neural Time-Invariant Causal Discovery from Time Series Data. IJCNN (2023)
Zhixu Du, Shiyu Li, Yuhao Wu, Xiangyu Jiang, Jingwei Sun, Qilin Zheng, Yongkai Wu, Ang Li, Hai (Helen) Li, Yiran Chen
SiDA: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models. CoRR (2023)
Aneesh Komanduri, Yongkai Wu, Feng Chen, Xintao Wu
Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms. CoRR (2023)
Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan
On Interpretable Anomaly Detection Using Causal Algorithmic Recourse. CoRR (2022)
Aneesh Komanduri, Yongkai Wu, Wen Huang, Feng Chen, Xintao Wu
SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge. Big Data (2022)
Karuna Bhaila, Yongkai Wu, Xintao Wu
Fair Collective Classification in Networked Data. Big Data (2022)
Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu
A Generative Adversarial Framework for Bounding Confounded Causal Effects. AAAI (2021)
Wen Huang, Yongkai Wu, Xintao Wu
Multi-cause Discrimination Analysis Using Potential Outcomes. SBP-BRiMS (2020)
Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu
Fair Multiple Decision Making Through Soft Interventions. NeurIPS (2020)
Wen Huang, Yongkai Wu, Lu Zhang, Xintao Wu
Fairness through Equality of Effort. WWW (Companion Volume) (2020)
Lu Zhang, Yongkai Wu, Xintao Wu
Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms. IEEE Trans. Knowl. Data Eng. 31 (11) (2019)
Yongkai Wu, Lu Zhang, Xintao Wu, Hanghang Tong
PC-Fairness: A Unified Framework for Measuring Causality-based Fairness. CoRR (2019)
Yongkai Wu, Lu Zhang, Xintao Wu, Hanghang Tong
PC-Fairness: A Unified Framework for Measuring Causality-based Fairness. NeurIPS (2019)
Yongkai Wu, Lu Zhang, Xintao Wu
Counterfactual Fairness: Unidentification, Bound and Algorithm. IJCAI (2019)
Depeng Xu, Yongkai Wu, Shuhan Yuan, Lu Zhang, Xintao Wu
Achieving Causal Fairness through Generative Adversarial Networks. IJCAI (2019)
Wen Huang, Yongkai Wu, Lu Zhang, Xintao Wu
Fairness through Equality of Effort. CoRR (2019)
Yongkai Wu, Lu Zhang, Xintao Wu
On Convexity and Bounds of Fairness-aware Classification. WWW (2019)
Yongkai Wu, Lu Zhang, Xintao Wu
Fairness-aware Classification: Criterion, Convexity, and Bounds. CoRR (2018)
Lu Zhang, Yongkai Wu, Xintao Wu
Achieving Non-Discrimination in Prediction. IJCAI (2018)
Yongkai Wu, Lu Zhang, Xintao Wu
On Discrimination Discovery and Removal in Ranked Data using Causal Graph. CoRR (2018)
Yongkai Wu, Lu Zhang, Xintao Wu
On Discrimination Discovery and Removal in Ranked Data using Causal Graph. KDD (2018)
Lu Zhang, Yongkai Wu, Xintao Wu
Achieving non-discrimination in prediction. CoRR (2017)
Lu Zhang, Yongkai Wu, Xintao Wu
Achieving Non-Discrimination in Data Release. KDD (2017)
Srinidhi Katla, Depeng Xu, Yongkai Wu, Qiuping Pan, Xintao Wu
DPWeka: Achieving Differential Privacy in WEKA. PAC (2017)
Lu Zhang, Yongkai Wu, Xintao Wu
A Causal Framework for Discovering and Removing Direct and Indirect Discrimination. IJCAI (2017)
Lu Zhang, Yongkai Wu, Xintao Wu
On Discrimination Discovery Using Causal Networks. SBP-BRiMS (2016)
Lu Zhang, Yongkai Wu, Xintao Wu
Situation Testing-Based Discrimination Discovery: A Causal Inference Approach. IJCAI (2016)
Lu Zhang, Yongkai Wu, Xintao Wu
A causal framework for discovering and removing direct and indirect discrimination. CoRR (2016)
Lu Zhang, Yongkai Wu, Xintao Wu
Achieving non-discrimination in data release. CoRR (2016)
Yongkai Wu, Xintao Wu
Using Loglinear Model for Discrimination Discovery and Prevention. DSAA (2016)