| Towards out-of-distribution generalization: A survey J Liu, Z Shen, Y He, X Zhang, R Xu, H Yu, P Cui arXiv preprint arXiv:2108.13624, 2021 | 620 | 2021 |
| Deep stable learning for out-of-distribution generalization X Zhang, P Cui, R Xu, L Zhou, Y He, Z Shen Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 326 | 2021 |
| Towards non-iid image classification: A dataset and baselines Y He, Z Shen, P Cui Pattern Recognition 110, 107383, 2021 | 214 | 2021 |
| Heterogeneous risk minimization J Liu, Z Hu, P Cui, B Li, Z Shen International Conference on Machine Learning, 6804-6814, 2021 | 151 | 2021 |
| Stable learning via sample reweighting Z Shen, P Cui, T Zhang, K Kunag Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5692-5699, 2020 | 151 | 2020 |
| Causally Regularized Learning with Agnostic Data Selection Bias Z Shen, P Cui, K Kuang, B Li, P Chen | 106 | 2018 |
| Nico++: Towards better benchmarking for domain generalization X Zhang, Y He, R Xu, H Yu, Z Shen, P Cui Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2023 | 88 | 2023 |
| Towards unsupervised domain generalization X Zhang, L Zhou, R Xu, P Cui, Z Shen, H Liu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 71 | 2022 |
| Causal inference meets machine learning P Cui, Z Shen, S Li, L Yao, Y Li, Z Chu, J Gao Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 62 | 2020 |
| Counterfactual prediction for bundle treatment H Zou, P Cui, B Li, Z Shen, J Ma, H Yang, Y He Advances in Neural Information Processing Systems 33, 19705-19715, 2020 | 61 | 2020 |
| Stable learning via differentiated variable decorrelation Z Shen, P Cui, J Liu, T Zhang, B Li, Z Chen Proceedings of the 26th acm sigkdd international conference on knowledge …, 2020 | 55 | 2020 |
| Algorithmic decision making with conditional fairness R Xu, P Cui, K Kuang, B Li, L Zhou, Z Shen, W Cui Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 41 | 2020 |
| Kernelized heterogeneous risk minimization J Liu, Z Hu, P Cui, B Li, Z Shen arXiv preprint arXiv:2110.12425, 2021 | 36 | 2021 |
| DARING: Differentiable causal discovery with residual independence Y He, P Cui, Z Shen, R Xu, F Liu, Y Jiang Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 35 | 2021 |
| Stable adversarial learning under distributional shifts J Liu, Z Shen, P Cui, L Zhou, K Kuang, B Li, Y Lin Proceedings of the AAAI Conference on Artificial Intelligence 35 (10), 8662-8670, 2021 | 32 | 2021 |
| A theoretical analysis on independence-driven importance weighting for covariate-shift generalization R Xu, X Zhang, Z Shen, T Zhang, P Cui International Conference on Machine Learning, 24803-24829, 2022 | 30 | 2022 |
| Why stable learning works? a theory of covariate shift generalization R Xu, P Cui, Z Shen, X Zhang, T Zhang arXiv preprint arXiv:2111.02355 2, 2021, 2021 | 24 | 2021 |
| Towards out-of-distribution generalization: A survey. arXiv 2021 Z Shen, J Liu, Y He, X Zhang, R Xu, H Yu, P Cui arXiv preprint arXiv:2108.13624, 0 | 21 | |
| Regulatory instruments for fair personalized pricing R Xu, X Zhang, P Cui, B Li, Z Shen, J Xu Proceedings of the ACM Web Conference 2022, 4-15, 2022 | 17 | 2022 |
| Stable learning via sparse variable independence H Yu, P Cui, Y He, Z Shen, Y Lin, R Xu, X Zhang Proceedings of the AAAI Conference on Artificial Intelligence 37 (9), 10998 …, 2023 | 14 | 2023 |
