| A sufficient condition for convergences of adam and rmsprop F Zou, L Shen, Z Jie, W Zhang, W Liu Proceedings of the IEEE/CVF Conference on computer vision and pattern …, 2019 | 495 | 2019 |
| Sparse Invariant Risk Minimization X Zhou*, Y Lin*, W Zhang*, T Zhang International Conference on Machine Learning, 27222-27244, 2022 | 68 | 2022 |
| Effective sparsification of neural networks with global sparsity constraint X Zhou*, W Zhang*, H Xu, T Zhang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 68 | 2021 |
| Model agnostic sample reweighting for out-of-distribution learning X Zhou, Y Lin, R Pi, W Zhang, R Xu, P Cui, T Zhang International Conference on Machine Learning, 27203-27221, 2022 | 49 | 2022 |
| Efficient Neural Network Training via Forward and Backward Propagation Sparsification X Zhou*, W Zhang*, Z Chen, S Diao, T Zhang Advances in Neural Information Processing Systems, 2021 | 44 | 2021 |
| Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction W Zhang, B Hong, W Liu, J Ye, D Cai, X He, J Wang Journal of Machine Learning Research 20 (121), 1-39, 2019 | 44 | 2019 |
| Hierarchical attention-based recurrent highway networks for time series prediction Y Tao, L Ma, W Zhang, J Liu, W Liu, Q Du arXiv preprint arXiv:1806.00685, 2018 | 36 | 2018 |
| Probabilistic Bilevel Coreset Selection X Zhou*, R Pi*, W Zhang*, Y Lin, Z Chen, T Zhang International Conference on Machine Learning, 27287-27302, 2022 | 33 | 2022 |
| Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction W Zhang, B Hong, W Liu, J Ye, D Cai, X He, J Wang Thirty-fourth International Conference on Machine Learning, 2017 | 31 | 2017 |
| Functional Annotation of Human Protein Coding Isoforms via Non-convex Multi-Instance Learning T Luo, W Zhang, S Qiu, Y Yang, D Yi, G Wang, J Ye, J Wang Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017 | 30 | 2017 |
| Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning X Yan, W Zhang, L Ma, W Liu, Q Wu Advances in Neural Information Processing Systems, 1580-1590, 2018 | 29 | 2018 |
| Sparse learning for stochastic composite optimization W Zhang, L Zhang, Y Hu, R Jin, D Cai, X He Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence 893, 2014 | 25 | 2014 |
| Fast adversarial training with adaptive step size Z Huang, Y Fan, C Liu, W Zhang, Y Zhang, M Salzmann, S Süsstrunk, ... IEEE Transactions on Image Processing, 2023 | 24 | 2023 |
| Convex formulation of overparameterized deep neural networks C Fang, Y Gu, W Zhang, T Zhang IEEE Transactions on Information Theory, 2022 | 22 | 2022 |
| Content-aware recommendation via dynamic heterogeneous graph convolutional network T Liang, L Ma, W Zhang, H Xu, C Xia, Y Yin Knowledge-Based Systems 251, 109185, 2022 | 20 | 2022 |
| Dynafed: Tackling client data heterogeneity with global dynamics R Pi, W Zhang, Y Xie, J Gao, X Wang, S Kim, Q Chen Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 18 | 2023 |
| Spurious feature diversification improves out-of-distribution generalization Y Lin, L Tan, Y Hao, H Wong, H Dong, W Zhang, Y Yang, T Zhang arXiv preprint arXiv:2309.17230, 2023 | 15 | 2023 |
| A Holistic View of Noise Transition Matrix in Deep Learning and Beyond LIN Yong, R Pi, W ZHANG, X Xia, J Gao, X Zhou, T Liu, B Han International Conference on Learning Representations, 2023 | 14* | 2023 |
| Accelerated Sparse Linear Regression via Random Projection W Zhang, L Zhang, R Jin, D Cai, X He | 13 | 2016 |
| Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning J Gao, R Pi, LIN Yong, H Xu, J Ye, Z Wu, W ZHANG, X Liang, Z Li, L Kong International Conference on Learning Representations, 2023 | 11 | 2023 |
Tong ZhangUIUCVerified email at tongzhang-ml.org
