| A causal framework for discovering and removing direct and indirect discrimination L Zhang, Y Wu, X Wu IJCAI 2017, 2017 | 231 | 2017 |
| Pc-fairness: A unified framework for measuring causality-based fairness Y Wu, L Zhang, X Wu, H Tong Advances in neural information processing systems 32, 2019 | 146 | 2019 |
| Counterfactual Fairness: Unidentification, Bound and Algorithm Y Wu, L Zhang, X Wu IJCAI 2019, 1438-1444, 2019 | 131 | 2019 |
| Achieving Causal Fairness through Generative Adversarial Networks D Xu, Y Wu, S Yuan, L Zhang, X Wu IJCAI 2019, 1452-1458, 2019 | 120 | 2019 |
| On convexity and bounds of fairness-aware classification Y Wu, L Zhang, X Wu The World Wide Web Conference, 3356-3362, 2019 | 100* | 2019 |
| Achieving non-discrimination in data release L Zhang, Y Wu, X Wu Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017 | 89 | 2017 |
| On discrimination discovery and removal in ranked data using causal graph Y Wu, L Zhang, X Wu Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 67 | 2018 |
| Situation Testing-Based Discrimination Discovery: A Causal Inference Approach. L Zhang, Y Wu, X Wu IJCAI 16, 2718-2724, 2016 | 66 | 2016 |
| Causal modeling-based discrimination discovery and removal: Criteria, bounds, and algorithms L Zhang, Y Wu, X Wu IEEE Transactions on Knowledge and Data Engineering 31 (11), 2035-2050, 2018 | 64 | 2018 |
| Fairness through equality of effort W Huang, Y Wu, L Zhang, X Wu Companion Proceedings of the Web Conference 2020, 743-751, 2020 | 47 | 2020 |
| Achieving non-discrimination in prediction L Zhang, Y Wu, X Wu IJCAI 2018, 2017 | 43 | 2017 |
| A generative adversarial framework for bounding confounded causal effects Y Hu, Y Wu, L Zhang, X Wu Proceedings of the AAAI Conference on Artificial Intelligence 35 (13), 12104 …, 2021 | 35 | 2021 |
| On discrimination discovery using causal networks L Zhang, Y Wu, X Wu Social, Cultural, and Behavioral Modeling: 9th International Conference, SBP …, 2016 | 25 | 2016 |
| Using loglinear model for discrimination discovery and prevention Y Wu, X Wu 2016 IEEE International Conference on Data Science and Advanced Analytics …, 2016 | 20 | 2016 |
| Fair multiple decision making through soft interventions Y Hu, Y Wu, L Zhang, X Wu Advances in neural information processing systems 33, 17965-17975, 2020 | 16 | 2020 |
| Achieving counterfactual fairness for anomaly detection X Han, L Zhang, Y Wu, S Yuan Pacific-Asia Conference on Knowledge Discovery and Data Mining, 55-66, 2023 | 12 | 2023 |
| Learning causally disentangled representations via the principle of independent causal mechanisms A Komanduri, Y Wu, F Chen, X Wu arXiv preprint arXiv:2306.01213, 2023 | 11 | 2023 |
| Scm-vae: Learning identifiable causal representations via structural knowledge A Komanduri, Y Wu, W Huang, F Chen, X Wu 2022 IEEE International Conference on Big Data (Big Data), 1014-1023, 2022 | 11 | 2022 |
| From identifiable causal representations to controllable counterfactual generation: A survey on causal generative modeling A Komanduri, X Wu, Y Wu, F Chen arXiv preprint arXiv:2310.11011, 2023 | 10 | 2023 |
| On root cause localization and anomaly mitigation through causal inference X Han, L Zhang, Y Wu, S Yuan Proceedings of the 32nd ACM International Conference on Information and …, 2023 | 8 | 2023 |
Xintao WuUniversity of ArkansasZweryfikowany adres z uark.edu
