Wu, Xintao

27 publications

ECML-PKDD 2025 Achieving Flexible Local Differential Privacy in Federated Learning via Influence Functions Alycia N. Carey, Xintao Wu

TMLR 2025 Multi-Modal Foundation Models for Computational Pathology: A Survey Dong Li, Guihong Wan, Xintao Wu, Xinyu Wu, Xiaohui Chen, Yi He, Zhong Chen, Peter K Sorger, Chen Zhao

ECML-PKDD 2024 Achieving Counterfactual Explanation for Sequence Anomaly Detection He Cheng, Depeng Xu, Shuhan Yuan, Xintao Wu

TMLR 2024 From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling Aneesh Komanduri, Xintao Wu, Yongkai Wu, Feng Chen

IJCAI 2024 Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms Aneesh Komanduri, Yongkai Wu, Feng Chen, Xintao Wu

AAAI 2024 Robustly Improving Bandit Algorithms with Confounded and Selection Biased Offline Data: A Causal Approach Wen Huang, Xintao Wu

IJCAI 2024 Supervised Algorithmic Fairness in Distribution Shifts: A Survey Minglai Shao, Dong Li, Chen Zhao, Xintao Wu, Yujie Lin, Qin Tian

NeurIPSW 2023 How to Backdoor HyperNetwork in Personalized Federated Learning? Phung Lai, Hai Phan, Issa Khalil, Abdallah Khreishah, Xintao Wu

NeurIPSW 2023 Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms Aneesh Komanduri, Yongkai Wu, Feng Chen, Xintao Wu

NeurIPSW 2023 Local Differential Privacy in Graph Neural Networks: A Reconstruction Approach Karuna Bhaila, Wen Huang, Yongkai Wu, Xintao Wu

AAAI 2022 Achieving Counterfactual Fairness for Causal Bandit Wen Huang, Lu Zhang, Xintao Wu

AAAI 2021 A Generative Adversarial Framework for Bounding Confounded Causal Effects Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu

NeurIPS 2020 Fair Multiple Decision Making Through Soft Interventions Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu

IJCAI 2019 Achieving Causal Fairness Through Generative Adversarial Networks Depeng Xu, Yongkai Wu, Shuhan Yuan, Lu Zhang, Xintao Wu

IJCAI 2019 Counterfactual Fairness: Unidentification, Bound and Algorithm Yongkai Wu, Lu Zhang, Xintao Wu

IJCAI 2019 Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness NhatHai Phan, Minh N. Vu, Yang Liu, Ruoming Jin, Dejing Dou, Xintao Wu, My T. Thai

AAAI 2019 One-Class Adversarial Nets for Fraud Detection Panpan Zheng, Shuhan Yuan, Xintao Wu, Jun Li, Aidong Lu

NeurIPS 2019 PC-Fairness: A Unified Framework for Measuring Causality-Based Fairness Yongkai Wu, Lu Zhang, Xintao Wu, Hanghang Tong

AAAI 2019 SAFE: A Neural Survival Analysis Model for Fraud Early Detection Panpan Zheng, Shuhan Yuan, Xintao Wu

IJCAI 2018 Achieving Non-Discrimination in Prediction Lu Zhang, Yongkai Wu, Xintao Wu

IJCAI 2017 A Causal Framework for Discovering and Removing Direct and Indirect Discrimination Lu Zhang, Yongkai Wu, Xintao Wu

MLJ 2017 Preserving Differential Privacy in Convolutional Deep Belief Networks NhatHai Phan, Xintao Wu, Dejing Dou

ECML-PKDD 2017 Wikipedia Vandal Early Detection: From User Behavior to User Embedding Shuhan Yuan, Panpan Zheng, Xintao Wu, Yang Xiang

AAAI 2016 Differential Privacy Preservation for Deep Auto-Encoders: An Application of Human Behavior Prediction NhatHai Phan, Yue Wang, Xintao Wu, Dejing Dou

IJCAI 2016 Situation Testing-Based Discrimination Discovery: A Causal Inference Approach Lu Zhang, Yongkai Wu, Xintao Wu

IJCAI 2015 Regression Model Fitting Under Differential Privacy and Model Inversion Attack Yue Wang, Cheng Si, Xintao Wu

IJCAI 2011 Line Orthogonality in Adjacency Eigenspace with Application to Community Partition Leting Wu, Xiaowei Ying, Xintao Wu, Zhi-Hua Zhou