| Learning disentangled semantic representation for domain adaptation R Cai, Z Li, P Wei, J Qiao, K Zhang, Z Hao IJCAI: proceedings of the conference 2019, 2060, 2019 | 147 | 2019 |
| Causal discovery from discrete data using hidden compact representation R Cai, J Qiao, K Zhang, Z Zhang, Z Hao Advances in neural information processing systems 31, 2018 | 49 | 2018 |
| Self: structural equational likelihood framework for causal discovery R Cai, J Qiao, Z Zhang, Z Hao Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 37 | 2018 |
| Causal discovery with cascade nonlinear additive noise models R Cai, J Qiao, K Zhang, Z Zhang, Z Hao arXiv preprint arXiv:1905.09442, 2019 | 33 | 2019 |
| THPs: Topological Hawkes processes for learning causal structure on event sequences R Cai, S Wu, J Qiao, Z Hao, K Zhang, X Zhang IEEE Transactions on Neural Networks and Learning Systems 35 (1), 479-493, 2022 | 24 | 2022 |
| Identification of linear latent variable model with arbitrary distribution Z Chen, F Xie, J Qiao, Z Hao, K Zhang, R Cai Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 6350-6357, 2022 | 20 | 2022 |
| FOM: Fourth-order moment based causal direction identification on the heteroscedastic data R Cai, J Ye, J Qiao, H Fu, Z Hao Neural Networks 124, 193-201, 2020 | 14 | 2020 |
| On the Role of Entropy-Based Loss for Learning Causal Structure With Continuous Optimization W Chen, J Qiao, R Cai, Z Hao IEEE Transactions on Neural Networks and Learning Systems, 2023 | 8 | 2023 |
| Thp: Topological hawkes processes for learning granger causality on event sequences R Cai, S Wu, J Qiao, Z Hao, K Zhang, X Zhang arXiv preprint arXiv:2105.10884, 2021 | 8 | 2021 |
| Causal discovery with confounding cascade nonlinear additive noise models J Qiao, R Cai, K Zhang, Z Zhang, Z Hao ACM Transactions on Intelligent Systems and Technology (TIST) 12 (6), 1-28, 2021 | 7 | 2021 |
| Structural hawkes processes for learning causal structure from discrete-time event sequences J Qiao, R Cai, S Wu, Y Xiang, K Zhang, Z Hao arXiv preprint arXiv:2305.05986, 2023 | 6 | 2023 |
| On the probability of necessity and sufficiency of explaining graph neural networks: A lower bound optimization approach R Cai, Y Zhu, X Chen, Y Fang, M Wu, J Qiao, Z Hao arXiv preprint arXiv:2212.07056, 2022 | 6 | 2022 |
| TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences Y Liu, R Cai, W Chen, J Qiao, Y Yan, Z Li, K Zhang, Z Hao Proceedings of the AAAI Conference on Artificial Intelligence 38 (18), 20491 …, 2024 | 4 | 2024 |
| REST: Debiased Social Recommendation via Reconstructing Exposure Strategies R Cai, F Wu, Z Li, J Qiao, W Chen, Y Hao, H Gu ACM Transactions on Knowledge Discovery from Data 18 (2), 1-24, 2023 | 4 | 2023 |
| Some General Identification Results for Linear Latent Hierarchical Causal Structure. Z Chen, F Xie, J Qiao, Z Hao, R Cai IJCAI, 3568-3576, 2023 | 3 | 2023 |
| Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning W Chen, R Cai, Z Yang, J Qiao, Y Yan, Z Li, Z Hao arXiv preprint arXiv:2405.03342, 2024 | 2 | 2024 |
| Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examples R Cai, Y Zhu, J Qiao, Z Liang, F Liu, Z Hao Proceedings of the AAAI Conference on Artificial Intelligence 38 (10), 11132 …, 2024 | 2 | 2024 |
| Identification of Causal Structure in the Presence of Missing Data with Additive Noise Model J Qiao, Z Chen, J Yu, R Cai, Z Hao Proceedings of the AAAI Conference on Artificial Intelligence 38 (18), 20516 …, 2024 | 2 | 2024 |
| Learning dynamic causal mechanisms from non-stationary data R Cai, L Huang, W Chen, J Qiao, Z Hao Applied Intelligence 53 (5), 5437-5448, 2023 | 2 | 2023 |
| Learning causal structures using hidden compact representation J Qiao, Y Bai, R Cai, Z Hao Neurocomputing 463, 328-333, 2021 | 2 | 2021 |
