| Techniques for interpretable machine learning M Du, N Liu, X Hu Communications of the ACM 63 (1), 68-77, 2019 | 1454 | 2019 |
| Explainability for large language models: A survey H Zhao, H Chen, F Yang, N Liu, H Deng, H Cai, S Wang, D Yin, M Du ACM Transactions on Intelligent Systems and Technology 15 (2), 1-38, 2024 | 320 | 2024 |
| Auggpt: Leveraging chatgpt for text data augmentation H Dai, Z Liu, W Liao, X Huang, Y Cao, Z Wu, L Zhao, S Xu, W Liu, N Liu, ... arXiv preprint arXiv:2302.13007, 2023 | 310* | 2023 |
| G-mixup: Graph data augmentation for graph classification X Han, Z Jiang, N Liu, X Hu International Conference on Machine Learning, 8230-8248, 2022 | 202 | 2022 |
| An embarrassingly simple approach for trojan attack in deep neural networks R Tang, M Du, N Liu, F Yang, X Hu Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 193 | 2020 |
| Towards explanation of dnn-based prediction with guided feature inversion M Du, N Liu, Q Song, X Hu Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 157 | 2018 |
| On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper) G Mai, W Huang, J Sun, S Song, D Mishra, N Liu, S Gao, T Liu, G Cong, ... ACM Transactions on Spatial Algorithms and Systems, 2024 | 149* | 2024 |
| Sparse-interest network for sequential recommendation Q Tan, J Zhang, J Yao, N Liu, J Zhou, H Yang, X Hu Proceedings of the 14th ACM international conference on web search and data …, 2021 | 145 | 2021 |
| Edits: Modeling and mitigating data bias for graph neural networks Y Dong, N Liu, B Jalaian, J Li Proceedings of the ACM web conference 2022, 1259-1269, 2022 | 137 | 2022 |
| Deep representation learning for social network analysis Q Tan, N Liu, X Hu Frontiers in big Data 2, 2, 2019 | 132 | 2019 |
| In-processing modeling techniques for machine learning fairness: A survey M Wan, D Zha, N Liu, N Zou ACM Transactions on Knowledge Discovery from Data 17 (3), 1-27, 2023 | 122 | 2023 |
| S2GAE: self-supervised graph autoencoders are generalizable learners with graph masking Q Tan, N Liu, X Huang, SH Choi, L Li, R Chen, X Hu Proceedings of the sixteenth ACM international conference on web search and …, 2023 | 117* | 2023 |
| Learning to hash with graph neural networks for recommender systems Q Tan, N Liu, X Zhao, H Yang, J Zhou, X Hu Proceedings of The Web Conference 2020, 1988-1998, 2020 | 106 | 2020 |
| Artificial general intelligence (AGI) for education E Latif, G Mai, M Nyaaba, X Wu, N Liu, G Lu, S Li, T Liu, X Zhai arXiv preprint arXiv:2304.12479, 2023 | 104* | 2023 |
| Is a single vector enough? exploring node polysemy for network embedding N Liu, Q Tan, Y Li, H Yang, J Zhou, X Hu Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 102 | 2019 |
| Contextual outlier interpretation N Liu, D Shin, X Hu Proceedings of the 27th International Joint Conference on Artificial …, 2018 | 99 | 2018 |
| Adversarial detection with model interpretation N Liu, H Yang, X Hu Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 81 | 2018 |
| Dynamic memory based attention network for sequential recommendation Q Tan, J Zhang, N Liu, X Huang, H Yang, J Zhou, X Hu Proceedings of the AAAI conference on artificial intelligence 35 (5), 4384-4392, 2021 | 74 | 2021 |
| Accelerated Local Anomaly Detection via Resolving Attributed Networks. N Liu, X Huang, X Hu IJCAI, 2337-2343, 2017 | 71 | 2017 |
| On interpretation of network embedding via taxonomy induction N Liu, X Huang, J Li, X Hu Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 67 | 2018 |
Xia "Ben" HuAssociate Professor of Computer Science, Rice UniversityVerified email at rice.edu