| Learning imbalanced datasets with label-distribution-aware margin loss K Cao, C Wei, A Gaidon, N Arechiga, T Ma Advances in neural information processing systems 32, 2019 | 1444 | 2019 |
| Towards explaining the regularization effect of initial large learning rate in training neural networks Y Li, C Wei, T Ma Advances in neural information processing systems 32, 2019 | 301 | 2019 |
| Regularization matters: Generalization and optimization of neural nets vs their induced kernel C Wei, JD Lee, Q Liu, T Ma Advances in Neural Information Processing Systems 32, 2019 | 269* | 2019 |
| Provable guarantees for self-supervised deep learning with spectral contrastive loss JZ HaoChen, C Wei, A Gaidon, T Ma Advances in Neural Information Processing Systems 34, 5000-5011, 2021 | 237 | 2021 |
| Theoretical analysis of self-training with deep networks on unlabeled data C Wei, K Shen, Y Chen, T Ma arXiv preprint arXiv:2010.03622, 2020 | 209 | 2020 |
| The implicit and explicit regularization effects of dropout C Wei, S Kakade, T Ma International conference on machine learning, 10181-10192, 2020 | 121 | 2020 |
| Generic 3d representation via pose estimation and matching AR Zamir, T Wekel, P Agrawal, C Wei, J Malik, S Savarese Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 102 | 2016 |
| Data-dependent sample complexity of deep neural networks via lipschitz augmentation C Wei, T Ma Advances in Neural Information Processing Systems 32, 2019 | 93 | 2019 |
| Shape matters: Understanding the implicit bias of the noise covariance JZ HaoChen, C Wei, J Lee, T Ma Conference on Learning Theory, 2315-2357, 2021 | 82 | 2021 |
| Why do pretrained language models help in downstream tasks? an analysis of head and prompt tuning C Wei, SM Xie, T Ma Advances in Neural Information Processing Systems 34, 16158-16170, 2021 | 75 | 2021 |
| Self-training avoids using spurious features under domain shift Y Chen, C Wei, A Kumar, T Ma Advances in Neural Information Processing Systems 33, 21061-21071, 2020 | 69 | 2020 |
| Statistically meaningful approximation: a case study on approximating turing machines with transformers C Wei, Y Chen, T Ma Advances in Neural Information Processing Systems 35, 12071-12083, 2022 | 52 | 2022 |
| Improved sample complexities for deep networks and robust classification via an all-layer margin C Wei, T Ma arXiv preprint arXiv:1910.04284, 2019 | 43 | 2019 |
| Improved sample complexities for deep neural networks and robust classification via an all-layer margin C Wei, T Ma International Conference on Learning Representations, 2019 | 36 | 2019 |
| Beyond separability: Analyzing the linear transferability of contrastive representations to related subpopulations JZ HaoChen, C Wei, A Kumar, T Ma Advances in neural information processing systems 35, 26889-26902, 2022 | 34 | 2022 |
| Certified robustness for deep equilibrium models via interval bound propagation C Wei, JZ Kolter International Conference on Learning Representations, 2021 | 20 | 2021 |
| Markov chain truncation for doubly-intractable inference C Wei, I Murray Artificial Intelligence and Statistics, 776-784, 2017 | 16 | 2017 |
| Meta-learning transferable representations with a single target domain H Liu, JZ HaoChen, C Wei, T Ma arXiv preprint arXiv:2011.01418, 2020 | 7 | 2020 |
| Max-margin works while large margin fails: Generalization without uniform convergence M Glasgow, C Wei, M Wootters, T Ma arXiv preprint arXiv:2206.07892, 2022 | 4 | 2022 |
| General bounds on satisfiability thresholds for random CSPs via fourier analysis C Wei, S Ermon Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 3 | 2017 |
