| A Survey on Model Compression for Large Language Models X Zhu, J Li*, Y Liu, C Ma, W Wang arXiv preprint arXiv:2308.07633, 2023 | 133 | 2023 |
| 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 (NeurIPS), 1586-1595, 2018 | 53 | 2018 |
| Multi-Class Learning using Unlabeled Samples: Theory and Algorithm J Li, Y Liu, R Yin, W Wang International Joint Conference on Artificial Intelligence (IJCAI), 2880-2886, 2019 | 21 | 2019 |
| Automated spectral kernel learning J Li, Y Liu, W Wang AAAI Conference on Artificial Intelligence (AAAI), 4618-4625, 2020 | 16 | 2020 |
| Neural architecture optimization with graph vae J Li, Y Liu, J Liu, W Wang arXiv preprint arXiv:2006.10310, 2020 | 15 | 2020 |
| Towards Sharp Analysis for Distributed Learning with Random Features J Li, Y Liu International Joint Conference on Artificial Intelligence (IJCAI), 3920-3928, 2023 | 13* | 2023 |
| Approximate Manifold Regularization: Scalable Algorithm and Generalization Analysis J Li, Y Liu, R Yin, W Wang International Joint Conference on Artificial Intelligence (IJCAI), 2887-2893, 2019 | 13 | 2019 |
| Convolutional spectral kernel learning with generalization guarantees J Li, Y Liu, W Wang Artificial Intelligence (AI) 313, 103803, 2022 | 10* | 2022 |
| Non-IID Federated Learning with Sharper Risk Bound B Wei, J Li*, Y Liu, W Wang IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022 | 7 | 2022 |
| Operation-level Progressive Differentiable Architecture Search X Zhu, J Li*, Y Liu, J Liao, W Wang 2021 IEEE International Conference on Data Mining (ICDM), 1559-1564, 2021 | 7 | 2021 |
| Federated Learning for Non-IID Data: From Theory to Algorithm B Wei, J Li*, Y Liu, W Wang Pacific Rim International Conference on Artificial Intelligence (PRICAI), 33-48, 2021 | 7 | 2021 |
| Improving Differentiable Architecture Search via self-distillation X Zhu, J Li*, Y Liu, W Wang Neural Networks 167, 656-667, 2023 | 6 | 2023 |
| Efficient Kernel Selection via Spectral Analysis J Li, Y Liu, H Lin, Y Yue, W Wang International Joint Conference on Artificial Intelligence (IJCAI), 2124-2130, 2017 | 6 | 2017 |
| Semi-supervised vector-valued learning: Improved bounds and algorithms J Li, Y Liu, W Wang Pattern Recognition 138, 109356, 2023 | 5* | 2023 |
| Optimal Convergence Rates for Distributed Nyström Approximation J Li, Y Liu, W Wang Journal of Machine Learning Research (JMLR) 24, 141(1-39), 2023 | 5 | 2023 |
| Optimal Convergence Rates for Agnostic Nyström Kernel Learning J Li, Y Liu, W Wang International Conference on Machine Learning (ICML), 2023 | 4 | 2023 |
| Ridgeless Regression with Random Features J Li, Y Liu, Y Zhang International Joint Conference on Artificial Intelligence (IJCAI), 3208-3214, 2022 | 3 | 2022 |
| Optimal Rates for Agnostic Distributed Learning J Li, Y Liu, W Wang IEEE Transactions on Information Theory (TIT), 2023 | 2 | 2023 |
| Sharper Utility Bounds for Differentially Private Models: Smooth and Non-smooth Y Kang, Y Liu, J Li, W Wang ACM International Conference on Information & Knowledge Management (CIKM …, 2022 | 2 | 2022 |
| High-dimensional analysis for Generalized Nonlinear Regression: From Asymptotics to Algorithm J Li, Y Liu, W Wang AAAI Conference on Artificial Intelligence (AAAI) 38 (12), 13500-13508, 2024 | 1 | 2024 |
Yong LiuRenmin University of China在 ruc.edu.cn 的电子邮件经过验证
