| On the identifiability of diagnostic classification models G Fang, J Liu, Z Ying Psychometrika 84, 19-40, 2019 | 58 | 2019 |
| Latent class analysis of recurrent events in problem-solving items H Xu, G Fang, Y Chen, J Liu, Z Ying Applied Psychological Measurement 42 (6), 478-498, 2018 | 38 | 2018 |
| A latent topic model with Markov transition for process data H Xu, G Fang, Z Ying British Journal of Mathematical and Statistical Psychology 73 (3), 474-505, 2020 | 23 | 2020 |
| Identifiability of bifactor models G Fang, J Guo, X Xu, Z Ying, S Zhang Statistica Sinica 31, 2309-2330, 2021 | 20 | 2021 |
| Group network hawkes process G Fang, G Xu, H Xu, X Zhu, Y Guan Journal of the American Statistical Association 119 (547), 2328-2344, 2024 | 19 | 2024 |
| Nearly optimal catoni’s m-estimator for infinite variance S Bhatt, G Fang, P Li, G Samorodnitsky International Conference on Machine Learning, 1925-1944, 2022 | 16 | 2022 |
| A note on sparse generalized eigenvalue problem Y Cai, G Fang, P Li Advances in Neural Information Processing Systems 34, 23036-23048, 2021 | 8 | 2021 |
| Offline change detection under contamination S Bhatt, G Fang, P Li Uncertainty in Artificial Intelligence, 191-201, 2022 | 7 | 2022 |
| On estimation in latent variable models G Fang, P Li International Conference on Machine Learning, 3100-3110, 2021 | 7 | 2021 |
| Regression with label permutation in generalized linear model G Fang, P Li International Conference on Machine Learning, 9716-9760, 2023 | 6 | 2023 |
| Minimax m-estimation under adversarial contamination S Bhatt, G Fang, P Li, G Samorodnitsky International Conference on Machine Learning, 1906-1924, 2022 | 6 | 2022 |
| Latent theme dictionary model for finding co-occurrent patterns in process data G Fang, Z Ying psychometrika 85, 775-811, 2020 | 6 | 2020 |
| Minimax M-estimation under adversarial corruption S Bhatt, G Fang, P Li, G Samorodnitsky Proceedings of the 39th International Conference on Machine Learning (ICML …, 2022 | 5 | 2022 |
| On variational inference in biclustering models G Fang, P Li International Conference on Machine Learning, 3111-3121, 2021 | 4 | 2021 |
| Network group hawkes process model H Xu, G Fang, X Zhu arXiv preprint arXiv:2002.08521, 2020 | 4 | 2020 |
| Copula for Instance-wise Feature Selection and Rank H Peng, G Fang, P Li Uncertainty in Artificial Intelligence, 1651-1661, 2023 | 3 | 2023 |
| Regret Analysis for RL using Renewal Bandit Feedback S Bhatt, G Fang, P Li, G Samorodnitsky 2022 IEEE Information Theory Workshop (ITW), 137-142, 2022 | 3 | 2022 |
| Efficient greedy coordinate descent via variable partitioning H Fang, G Fang, T Yu, P Li Uncertainty in Artificial Intelligence, 547-557, 2021 | 3 | 2021 |
| On provable privacy vulnerabilities of graph representations R Wu, G Fang, Q Pan, M Zhang, T Liu, W Wang arXiv preprint arXiv:2402.04033, 2024 | 2 | 2024 |
| Online estimation and community detection of network point processes for event streams G Fang, OG Ward, T Zheng Statistics and Computing 34 (1), 35, 2024 | 2 | 2024 |
