921 citations · 1.4k across the 54 of their papers we have counts for
9 papers · 1 filter
Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers
Fan Yang, Hongyang R. Zhang, Sen Wu +2
The problem of learning one task using samples from another task is central to transfer learning. In this paper, we focus on answering the following question: when does combining t…
An Analysis of Robustness of Non-Lipschitz Networks
Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma +1
Despite significant advances, deep networks remain highly susceptible to adversarial attack. One fundamental challenge is that small input perturbations can often produce large mov…
Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients
Yifei Huang, Yaodong Yu, Hongyang Zhang +2
In this paper we introduce a provably stable architecture for Neural Ordinary Differential Equations (ODEs) which achieves non-trivial adversarial robustness under white-box advers…
Learning Over-Parametrized Two-Layer ReLU Neural Networks beyond NTK
Yuanzhi Li, Tengyu Ma, Hongyang R. Zhang
We consider the dynamic of gradient descent for learning a two-layer neural network. We assume the input is drawn from a Gaussian distribution and the label of $…
Understanding and Improving Information Transfer in Multi-Task Learning
Sen Wu, Hongyang R. Zhang, Christopher Ré
We investigate multi-task learning approaches that use a shared feature representation for all tasks. To better understand the transfer of task information, we study an architectur…
On the Generalization Effects of Linear Transformations in Data Augmentation
Sen Wu, Hongyang R. Zhang, Gregory Valiant +1
Data augmentation is a powerful technique to improve performance in applications such as image and text classification tasks. Yet, there is little rigorous understanding of why and…
Random Smoothing Might be Unable to Certify Robustness for High-Dimensional Images
Avrim Blum, Travis Dick, Naren Manoj +1
We show a hardness result for random smoothing to achieve certified adversarial robustness against attacks in the ball of radius when . Although random smoothing…
A Closer Look at Accuracy vs. Robustness
Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang +2
Current methods for training robust networks lead to a drop in test accuracy, which has led prior works to posit that a robustness-accuracy tradeoff may be inevitable in deep learn…
Self-Adaptive Training: beyond Empirical Risk Minimization
Lang Huang, Chao Zhang, Hongyang Zhang
We propose self-adaptive training---a new training algorithm that dynamically corrects problematic training labels by model predictions without incurring extra computational cost--…