Publications (41)
Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition
Yaodong Yu, Zitong Yang, Edgar Dobriban +2
Adversarially trained models exhibit a large generalization gap: they can interpolate the training set even for large perturbation radii, but at the cost of large test error on cle…
Adventurer: Optimizing Vision Mamba Architecture Designs for Efficiency
Feng Wang, Timing Yang, Yaodong Yu +7
In this work, we introduce the Adventurer series models where we treat images as sequences of patch tokens and employ uni-directional language models to learn visual representation…
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback
Tianyi Lin, Aldo Pacchiano, Yaodong Yu +1
Motivated by applications to online learning in sparse estimation and Bayesian optimization, we consider the problem of online unconstrained nonsubmodular minimization with delayed…
Token Statistics Transformer: Linear-Time Attention via Variational Rate Reduction
Ziyang Wu, Tianjiao Ding, Yifu Lu +6
The attention operator is arguably the key distinguishing factor of transformer architectures, which have demonstrated state-of-the-art performance on a variety of tasks. However,…
Predicting Out-of-Distribution Error with the Projection Norm
Yaodong Yu, Zitong Yang, Alexander Wei +2
We propose a metric -- Projection Norm -- to predict a model's performance on out-of-distribution (OOD) data without access to ground truth labels. Projection Norm first uses model…
Scaling Laws in Patchification: An Image Is Worth 50,176 Tokens And More
Feng Wang, Yaodong Yu, Guoyizhe Wei +4
Since the introduction of Vision Transformer (ViT), patchification has long been regarded as a de facto image tokenization approach for plain visual architectures. By compressing t…