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2020 – today
- 2023
[c18]Margalit Glasgow, Colin Wei, Mary Wootters, Tengyu Ma:
Max-Margin Works while Large Margin Fails: Generalization without Uniform Convergence. ICLR 2023- 2022
- [b1]Colin Wei:
Theoretical insights on generalization in supervised and self-supervised deep learning. Stanford University, USA, 2022 - [c17]Colin Wei, J. Zico Kolter:
Certified Robustness for Deep Equilibrium Models via Interval Bound Propagation. ICLR 2022 - [c16]Jeff Z. HaoChen, Colin Wei, Ananya Kumar, Tengyu Ma:
Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations. NeurIPS 2022 - [c15]Colin Wei, Yining Chen, Tengyu Ma:
Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers. NeurIPS 2022 - [i18]Jeff Z. HaoChen, Colin Wei, Ananya Kumar, Tengyu Ma:
Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations. CoRR abs/2204.02683 (2022) - [i17]Margalit Glasgow, Colin Wei, Mary Wootters, Tengyu Ma:
Max-Margin Works while Large Margin Fails: Generalization without Uniform Convergence. CoRR abs/2206.07892 (2022) - 2021
- [c14]Jeff Z. HaoChen, Colin Wei, Jason D. Lee, Tengyu Ma:
Shape Matters: Understanding the Implicit Bias of the Noise Covariance. COLT 2021: 2315-2357 - [c13]Colin Wei, Kendrick Shen, Yining Chen, Tengyu Ma:
Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data. ICLR 2021 - [c12]Jeff Z. HaoChen, Colin Wei, Adrien Gaidon, Tengyu Ma:
Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss. NeurIPS 2021: 5000-5011 - [c11]Colin Wei, Sang Michael Xie, Tengyu Ma:
Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning. NeurIPS 2021: 16158-16170 - [i16]Jeff Z. HaoChen, Colin Wei, Adrien Gaidon, Tengyu Ma:
Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss. CoRR abs/2106.04156 (2021) - [i15]Colin Wei, Sang Michael Xie, Tengyu Ma:
Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning. CoRR abs/2106.09226 (2021) - [i14]Colin Wei, Yining Chen, Tengyu Ma:
Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers. CoRR abs/2107.13163 (2021) - 2020
- [c10]Colin Wei, Tengyu Ma:
Improved Sample Complexities for Deep Neural Networks and Robust Classification via an All-Layer Margin. ICLR 2020 - [c9]Colin Wei, Sham M. Kakade, Tengyu Ma:
The Implicit and Explicit Regularization Effects of Dropout. ICML 2020: 10181-10192 - [c8]Yining Chen, Colin Wei, Ananya Kumar, Tengyu Ma:
Self-training Avoids Using Spurious Features Under Domain Shift. NeurIPS 2020 - [i13]Colin Wei, Sham M. Kakade, Tengyu Ma:
The Implicit and Explicit Regularization Effects of Dropout. CoRR abs/2002.12915 (2020) - [i12]Jeff Z. HaoChen, Colin Wei, Jason D. Lee, Tengyu Ma:
Shape Matters: Understanding the Implicit Bias of the Noise Covariance. CoRR abs/2006.08680 (2020) - [i11]Yining Chen, Colin Wei, Ananya Kumar, Tengyu Ma:
Self-training Avoids Using Spurious Features Under Domain Shift. CoRR abs/2006.10032 (2020) - [i10]Colin Wei, Kendrick Shen, Yining Chen, Tengyu Ma:
Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data. CoRR abs/2010.03622 (2020) - [i9]Hong Liu, Jeff Z. HaoChen, Colin Wei, Tengyu Ma:
Meta-learning Transferable Representations with a Single Target Domain. CoRR abs/2011.01418 (2020)
2010 – 2019
- 2019
- [c7]Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Aréchiga, Tengyu Ma:
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. NeurIPS 2019: 1565-1576 - [c6]Colin Wei, Jason D. Lee, Qiang Liu, Tengyu Ma:
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel. NeurIPS 2019: 9709-9721 - [c5]Colin Wei, Tengyu Ma:
Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation. NeurIPS 2019: 9722-9733 - [c4]Yuanzhi Li, Colin Wei, Tengyu Ma:
Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks. NeurIPS 2019: 11669-11680 - [i8]Colin Wei, Tengyu Ma:
Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation. CoRR abs/1905.03684 (2019) - [i7]Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Aréchiga, Tengyu Ma:
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. CoRR abs/1906.07413 (2019) - [i6]Yuanzhi Li, Colin Wei, Tengyu Ma:
Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks. CoRR abs/1907.04595 (2019) - [i5]Colin Wei, Tengyu Ma:
Improved Sample Complexities for Deep Networks and Robust Classification via an All-Layer Margin. CoRR abs/1910.04284 (2019) - 2018
- [i4]Colin Wei, Jason D. Lee, Qiang Liu, Tengyu Ma:
On the Margin Theory of Feedforward Neural Networks. CoRR abs/1810.05369 (2018) - 2017
- [c3]Colin Wei, Stefano Ermon:
General Bounds on Satisfiability Thresholds for Random CSPs via Fourier Analysis. AAAI 2017: 3958-3966 - [c2]Colin Wei, Iain Murray:
Markov Chain Truncation for Doubly-Intractable Inference. AISTATS 2017: 776-784 - [i3]Colin Wei, Stefano Ermon:
General Bounds on Satisfiability Thresholds for Random CSPs via Fourier Analysis. CoRR abs/1701.06258 (2017) - [i2]Amir R. Zamir, Tilman Wekel, Pulkit Agrawal, Colin Wei, Jitendra Malik, Silvio Savarese:
Generic 3D Representation via Pose Estimation and Matching. CoRR abs/1710.08247 (2017) - 2016
- [c1]Amir R. Zamir, Tilman Wekel, Pulkit Agrawal, Colin Wei, Jitendra Malik, Silvio Savarese:
Generic 3D Representation via Pose Estimation and Matching. ECCV (3) 2016: 535-553 - [i1]Colin Wei, Iain Murray:
Markov Chain Truncation for Doubly-Intractable Inference. CoRR abs/1610.05672 (2016)
Coauthor Index
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