Colin Wei

According to our database1, Colin Wei authored at least 23 papers between 2016 and 2024.

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Bibliography

2024
GPT-4o System Card.
CoRR, 2024

2023
Max-Margin Works while Large Margin Fails: Generalization without Uniform Convergence.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Theoretical insights on generalization in supervised and self-supervised deep learning.
PhD thesis, 2022

Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Certified Robustness for Deep Equilibrium Models via Interval Bound Propagation.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data.
Proceedings of the 9th International Conference on Learning Representations, 2021

Shape Matters: Understanding the Implicit Bias of the Noise Covariance.
Proceedings of the Conference on Learning Theory, 2021

2020
Meta-learning Transferable Representations with a Single Target Domain.
CoRR, 2020

Self-training Avoids Using Spurious Features Under Domain Shift.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

The Implicit and Explicit Regularization Effects of Dropout.
Proceedings of the 37th International Conference on Machine Learning, 2020

Improved Sample Complexities for Deep Neural Networks and Robust Classification via an All-Layer Margin.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Improved Sample Complexities for Deep Networks and Robust Classification via an All-Layer Margin.
CoRR, 2019

Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018
On the Margin Theory of Feedforward Neural Networks.
CoRR, 2018

2017
Markov Chain Truncation for Doubly-Intractable Inference.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

General Bounds on Satisfiability Thresholds for Random CSPs via Fourier Analysis.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
Generic 3D Representation via Pose Estimation and Matching.
Proceedings of the Computer Vision - ECCV 2016, 2016


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