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ORCIDColin Wei
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2020 – today
- 2024
[i19]Aaron Hurst, Adam Lerer, Adam P. Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, Aleksander Madry, Alex Baker-Whitcomb, Alex Beutel, Alex Borzunov, Alex Carney, Alex Chow, Alex Kirillov, Alex Nichol, Alex Paino, Alex Renzin, Alex Tachard Passos, Alexander Kirillov, Alexi Christakis, Alexis Conneau, Ali Kamali, Allan Jabri, Allison Moyer, Allison Tam, Amadou Crookes, Amin Tootoonchian, Ananya Kumar, Andrea Vallone, Andrej Karpathy, Andrew Braunstein, Andrew Cann, Andrew Codispoti, Andrew Galu, Andrew Kondrich, Andrew Tulloch, Andrey Mishchenko, Angela Baek, Angela Jiang, Antoine Pelisse, Antonia Woodford, Anuj Gosalia, Arka Dhar, Ashley Pantuliano, Avi Nayak, Avital Oliver, Barret Zoph, Behrooz Ghorbani, Ben Leimberger, Ben Rossen, Ben Sokolowsky, Ben Wang, Benjamin Zweig, Beth Hoover, Blake Samic, Bob McGrew, Bobby Spero, Bogo Giertler, Bowen Cheng, Brad Lightcap, Brandon Walkin, Brendan Quinn, Brian Guarraci, Brian Hsu, Bright Kellogg, Brydon Eastman, Camillo Lugaresi, Carroll L. Wainwright, Cary Bassin, Cary Hudson, Casey Chu, Chad Nelson, Chak Li, Chan Jun Shern, Channing Conger, Charlotte Barette, Chelsea Voss, Chen Ding, Cheng Lu, Chong Zhang, Chris Beaumont, Chris Hallacy, Chris Koch, Christian Gibson, Christina Kim, Christine Choi, Christine McLeavey, Christopher Hesse, Claudia Fischer, Clemens Winter, Coley Czarnecki, Colin Jarvis, Colin Wei, Constantin Koumouzelis, Dane Sherburn:
GPT-4o System Card. CoRR abs/2410.21276 (2024)- 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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last updated on 2024-12-03 20:32 CET by the dblp team
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