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Colin Wei
Colin Wei
Verified email at stanford.edu - Homepage
Title
Cited by
Cited by
Year
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
15192019
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
3182019
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
277*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
2522021
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
2242020
The implicit and explicit regularization effects of dropout
C Wei, S Kakade, T Ma
International conference on machine learning, 10181-10192, 2020
1262020
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
1022016
Data-dependent sample complexity of deep neural networks via lipschitz augmentation
C Wei, T Ma
Advances in Neural Information Processing Systems 32, 2019
1002019
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
852021
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
762021
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
722020
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
602022
Improved sample complexities for deep networks and robust classification via an all-layer margin
C Wei, T Ma
arXiv preprint arXiv:1910.04284, 2019
442019
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
382019
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
372022
Certified robustness for deep equilibrium models via interval bound propagation
C Wei, JZ Kolter
International Conference on Learning Representations, 2021
202021
Markov chain truncation for doubly-intractable inference
C Wei, I Murray
Artificial Intelligence and Statistics, 776-784, 2017
162017
Meta-learning transferable representations with a single target domain
H Liu, JZ HaoChen, C Wei, T Ma
arXiv preprint arXiv:2011.01418, 2020
72020
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
42022
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
32017
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Articles 1–20