Colin Wei
16 papers · 2017–2023 · 5 conferences · across top CS/AI conferences
Achievements
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πΊοΈ Taxonomy Completionist (29) π Interdisciplinary Bridge π Academic Marathon (6) π Conference Polyglot (5) π§ Keyword Pioneer
π
Conference Polyglot
(5)
π
Academic Marathon
(6)
π
The Namer
π€
Dynamic Duo
(14)
π
Triple Crown
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Trend Setter
π₯
Unstoppable
(5)
π
Century Club
(16)
β
The Questioner
Conferences
NIPS (9)
ICLR (4)
AISTATS (1)
COLT (1)
ICML (1)
Top co-authors
Keywords
neural network
(4)
stochastic gradient descent
(3)
representation learning
(3)
contrastive learning
(2)
sample complexity
(2)
generalization bound
(2)
implicit regularization
(2)
domain adaptation
(1)
transfer learning
(1)
neural tangent kernel
(1)
unsupervised domain adaptation
(1)
markov chain monte carlo
(1)
importance sampling
(1)
partition function
(1)
hidden markov model
(1)
data augmentation
(1)
spectral decomposition
(1)
rademacher complexity
(1)
gradient descent
(1)
label noise
(1)
Papers
Max-Margin Works while Large Margin Fails: Generalization without Uniform Convergence
ICLR 2023
Certified Robustness for Deep Equilibrium Models via Interval Bound Propagation
ICLR 2022
Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers
NIPS 2022
Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations
NIPS 2022
Shape Matters: Understanding the Implicit Bias of the Noise Covariance
COLT 2021
Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss
NIPS 2021
Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning
NIPS 2021
Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data
ICLR 2021
The Implicit and Explicit Regularization Effects of Dropout
ICML 2020
Self-training Avoids Using Spurious Features Under Domain Shift
NIPS 2020
Improved Sample Complexities for Deep Neural Networks and Robust Classification via an All-Layer Margin
ICLR 2020
Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation
NIPS 2019
Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks
NIPS 2019
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel
NIPS 2019
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
NIPS 2019
Markov Chain Truncation for Doubly-Intractable Inference
AISTATS 2017