| Multi-class learning: From theory to algorithm J Li, Y Liu, R Yin, H Zhang, L Ding, W Wang Advances in Neural Information Processing Systems 31, 2018 | 52 | 2018 |
| Sketch kernel ridge regression using circulant matrix: Algorithm and theory R Yin, Y Liu, W Wang, D Meng IEEE transactions on neural networks and learning systems 31 (9), 3512-3524, 2019 | 21 | 2019 |
| Multi-Class Learning using Unlabeled Samples: Theory and Algorithm. J Li, Y Liu, R Yin, W Wang IJCAI, 2880-2886, 2019 | 21 | 2019 |
| Divide-and-conquer learning with nyström: Optimal rate and algorithm R Yin, Y Liu, L Lu, W Wang, D Meng Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 6696-6703, 2020 | 18 | 2020 |
| Triangle counting accelerations: From algorithm to in-memory computing architecture X Wang, J Yang, Y Zhao, X Jia, R Yin, X Chen, G Qu, W Zhao IEEE Transactions on Computers 71 (10), 2462-2472, 2021 | 15 | 2021 |
| Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis. J Li, Y Liu, R Yin, W Wang IJCAI, 2887-2893, 2019 | 13 | 2019 |
| Distributed Nystrom Kernel Learning with Communications Rong Yin, Yong Liu, Weiping Wang, Dan Meng Proceedings of the 28th International Conference on Machine Learning (ICML), 2021 | 11* | 2021 |
| Extremely sparse Johnson-Lindenstrauss transform: From theory to algorithm R Yin, Y Liu, W Wang, D Meng 2020 IEEE International Conference on Data Mining (ICDM), 1376-1381, 2020 | 5 | 2020 |
| Scalable Kernel -Means with Randomized Sketching: From Theory to Algorithm Rong Yin, Yong Liu, Weiping Wang, Dan Meng IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022 | 4* | 2022 |
| Distributed randomized sketching kernel learning R Yin, Y Liu, D Meng Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 8883-8891, 2022 | 2 | 2022 |
| Randomized Sketches for Clustering: Fast and Optimal Kernel -Means Rong Yin, Yong Liu, Weiping Wang, Dan Meng In NeurIPS, 2022 | 2* | 2022 |
| ASWT-SGNN: Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network R Liu, R Yin, Y Liu, W Wang Proceedings of the AAAI Conference on Artificial Intelligence 38 (12), 13990 …, 2024 | 1 | 2024 |
| Unbiased and augmentation-free self-supervised graph representation learning R Liu, R Yin, Y Liu, W Wang Pattern Recognition 149, 110274, 2024 | | 2024 |
| Distributed Least Square Ranking with Random Features R Yin, Y Liu, W Wang, D Meng | | 2022 |
| Hashing Based Prediction for Large-Scale Kernel Machine L Lu, R Yin, Y Liu, W Wang Computational Science–ICCS 2020: 20th International Conference, Amsterdam …, 2020 | | 2020 |
