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2020 – today
- 2024
[j2]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)- [c30]Yaowei Hu, Yongkai Wu
, Lu Zhang:
Long-Term Fair Decision Making through Deep Generative Models. AAAI 2024: 22114-22122 - [c29]Aneesh Komanduri, Yongkai Wu, Feng Chen, Xintao Wu:
Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms. IJCAI 2024: 4308-4316 - [c28]Yucong Dai, Xiangyu Jiang, Yaowei Hu, Lu Zhang, Yongkai Wu:
Fair Weak-Supervised Learning: A Multiple-Instance Learning Approach. IJCNN 2024: 1-7 - [c27]Shuang Wang, Yongkai Wu:
Achieving Equalized Explainability Through Data Reconstruction. IJCNN 2024: 1-8 - [c26]Shuang Wang, Yongkai Wu:
Achieving Fairness through Constrained Recourse. IJCNN 2024: 1-8 - [c25]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 - [c24]Karuna Bhaila, Wen Huang, Yongkai Wu, Xintao Wu:
Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach. SDM 2024: 1-9 - [i18]Yaowei Hu, Yongkai Wu, Lu Zhang:
Long-Term Fair Decision Making through Deep Generative Models. CoRR abs/2401.11288 (2024) - [i17]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 abs/2405.00705 (2024) - [i16]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 abs/2405.08125 (2024) - 2023
- [c23]Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan:
On Root Cause Localization and Anomaly Mitigation through Causal Inference. CIKM 2023: 699-708 - [c22]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:
Artificial Intelligence for Climate Smart Forestry: A Forward Looking Vision. CogMI 2023: 1-10 - [c21]Saima Absar, Yongkai Wu, Lu Zhang:
Neural Time-Invariant Causal Discovery from Time Series Data. IJCNN 2023: 1-8 - [c20]Xiangyu Jiang, Yucong Dai, Yongkai Wu:
Fair Selection through Kernel Density Estimation. IJCNN 2023: 1-8 - [c19]Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan:
Achieving Counterfactual Fairness for Anomaly Detection. PAKDD (1) 2023: 55-66 - [i15]Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan:
Achieving Counterfactual Fairness for Anomaly Detection. CoRR abs/2303.02318 (2023) - [i14]Aneesh Komanduri, Yongkai Wu, Feng Chen, Xintao Wu:
Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms. CoRR abs/2306.01213 (2023) - [i13]Karuna Bhaila, Wen Huang, Yongkai Wu, Xintao Wu:
Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach. CoRR abs/2309.08569 (2023) - [i12]Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan:
Algorithmic Recourse for Anomaly Detection in Multivariate Time Series. CoRR abs/2309.16896 (2023) - [i11]Aneesh Komanduri, Xintao Wu, Yongkai Wu, Feng Chen:
From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling. CoRR abs/2310.11011 (2023) - [i10]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 abs/2310.18859 (2023) - [i9]Yucong Dai, Gen Li, Feng Luo, Xiaolong Ma, Yongkai Wu:
Coupling Fairness and Pruning in a Single Run: a Bi-level Optimization Perspective. CoRR abs/2312.10181 (2023) - 2022
- [c18]Aneesh Komanduri, Yongkai Wu, Wen Huang, Feng Chen, Xintao Wu:
SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge. IEEE Big Data 2022: 1014-1023 - [c17]Karuna Bhaila, Yongkai Wu, Xintao Wu:
Fair Collective Classification in Networked Data. IEEE Big Data 2022: 1415-1424 - [i8]Xiao Han, Lu Zhang, Yongkai Wu, Shuhan Yuan:
On Interpretable Anomaly Detection Using Causal Algorithmic Recourse. CoRR abs/2212.04031 (2022) - 2021
- [c16]Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu:
A Generative Adversarial Framework for Bounding Confounded Causal Effects. AAAI 2021: 12104-12112 - 2020
- [c15]Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu:
Fair Multiple Decision Making Through Soft Interventions. NeurIPS 2020 - [c14]Wen Huang, Yongkai Wu, Xintao Wu:
Multi-cause Discrimination Analysis Using Potential Outcomes. SBP-BRiMS 2020: 224-234 - [c13]Wen Huang, Yongkai Wu, Lu Zhang, Xintao Wu:
Fairness through Equality of Effort. WWW (Companion Volume) 2020: 743-751
2010 – 2019
- 2019
- [j1]Lu Zhang, Yongkai Wu, Xintao Wu:
Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms. IEEE Trans. Knowl. Data Eng. 31(11): 2035-2050 (2019) - [c12]Yongkai Wu, Lu Zhang, Xintao Wu:
Counterfactual Fairness: Unidentification, Bound and Algorithm. IJCAI 2019: 1438-1444 - [c11]Depeng Xu, Yongkai Wu, Shuhan Yuan, Lu Zhang, Xintao Wu:
Achieving Causal Fairness through Generative Adversarial Networks. IJCAI 2019: 1452-1458 - [c10]Yongkai Wu, Lu Zhang, Xintao Wu, Hanghang Tong:
PC-Fairness: A Unified Framework for Measuring Causality-based Fairness. NeurIPS 2019: 3399-3409 - [c9]Yongkai Wu, Lu Zhang, Xintao Wu:
On Convexity and Bounds of Fairness-aware Classification. WWW 2019: 3356-3362 - [i7]Yongkai Wu, Lu Zhang, Xintao Wu, Hanghang Tong:
PC-Fairness: A Unified Framework for Measuring Causality-based Fairness. CoRR abs/1910.12586 (2019) - [i6]Wen Huang, Yongkai Wu, Lu Zhang, Xintao Wu:
Fairness through Equality of Effort. CoRR abs/1911.08292 (2019) - 2018
- [c8]Lu Zhang, Yongkai Wu, Xintao Wu:
Achieving Non-Discrimination in Prediction. IJCAI 2018: 3097-3103 - [c7]Yongkai Wu, Lu Zhang, Xintao Wu:
On Discrimination Discovery and Removal in Ranked Data using Causal Graph. KDD 2018: 2536-2544 - [i5]Yongkai Wu, Lu Zhang, Xintao Wu:
On Discrimination Discovery and Removal in Ranked Data using Causal Graph. CoRR abs/1803.01901 (2018) - [i4]Yongkai Wu, Lu Zhang, Xintao Wu:
Fairness-aware Classification: Criterion, Convexity, and Bounds. CoRR abs/1809.04737 (2018) - 2017
- [c6]Lu Zhang, Yongkai Wu, Xintao Wu:
A Causal Framework for Discovering and Removing Direct and Indirect Discrimination. IJCAI 2017: 3929-3935 - [c5]Lu Zhang, Yongkai Wu, Xintao Wu:
Achieving Non-Discrimination in Data Release. KDD 2017: 1335-1344 - [c4]Srinidhi Katla, Depeng Xu, Yongkai Wu, Qiuping Pan, Xintao Wu:
DPWeka: Achieving Differential Privacy in WEKA. PAC 2017: 184-185 - [i3]Lu Zhang, Yongkai Wu, Xintao Wu:
Achieving non-discrimination in prediction. CoRR abs/1703.00060 (2017) - 2016
- [c3]Yongkai Wu, Xintao Wu:
Using Loglinear Model for Discrimination Discovery and Prevention. DSAA 2016: 110-119 - [c2]Lu Zhang, Yongkai Wu, Xintao Wu:
Situation Testing-Based Discrimination Discovery: A Causal Inference Approach. IJCAI 2016: 2718-2724 - [c1]Lu Zhang, Yongkai Wu, Xintao Wu:
On Discrimination Discovery Using Causal Networks. SBP-BRiMS 2016: 83-93 - [i2]Lu Zhang, Yongkai Wu, Xintao Wu:
Achieving non-discrimination in data release. CoRR abs/1611.07438 (2016) - [i1]Lu Zhang, Yongkai Wu, Xintao Wu:
A causal framework for discovering and removing direct and indirect discrimination. CoRR abs/1611.07509 (2016)
Coauthor Index
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