124 citations · 276 across the 23 of their papers we have counts for
11 papers · 1 filter
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis
Baihe Huang, Xiaoxiao Li, Zhao Song +1
Federated Learning (FL) is an emerging learning scheme that allows different distributed clients to train deep neural networks together without data sharing. Neural networks have b…
Symmetric Sparse Boolean Matrix Factorization and Applications
Sitan Chen, Zhao Song, Runzhou Tao +1
In this work, we study a variant of nonnegative matrix factorization where we wish to find a symmetric factorization of a given input matrix into a sparse, Boolean matrix. Formally…
InstaHide's Sample Complexity When Mixing Two Private Images
Baihe Huang, Zhao Song, Runzhou Tao +3
Training neural networks usually require large numbers of sensitive training data, and how to protect the privacy of training data has thus become a critical topic in deep learning…
On InstaHide, Phase Retrieval, and Sparse Matrix Factorization
Sitan Chen, Xiaoxiao Li, Zhao Song +1
In this work, we examine the security of InstaHide, a scheme recently proposed by [Huang, Song, Li and Arora, ICML'20] for preserving the security of private datasets in the contex…
MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery
Xiaoxiao Li, Yangsibo Huang, Binghui Peng +2
To address the issue that deep neural networks (DNNs) are vulnerable to model inversion attacks, we design an objective function, which adjusts the separability of the hidden data…
Generalized Leverage Score Sampling for Neural Networks
Jason D. Lee, Ruoqi Shen, Zhao Song +2
Leverage score sampling is a powerful technique that originates from theoretical computer science, which can be used to speed up a large number of fundamental questions, e.g. linea…
Privacy-preserving Learning via Deep Net Pruning
Yangsibo Huang, Yushan Su, Sachin Ravi +3
This paper attempts to answer the question whether neural network pruning can be used as a tool to achieve differential privacy without losing much data utility. As a first step to…
Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality
Yi Zhang, Orestis Plevrakis, Simon S. Du +3
Adversarial training is a popular method to give neural nets robustness against adversarial perturbations. In practice adversarial training leads to low robust training loss. Howev…
Meta-learning for mixed linear regression
Weihao Kong, Raghav Somani, Zhao Song +2
In modern supervised learning, there are a large number of tasks, but many of them are associated with only a small amount of labeled data. These include data from medical image pr…
Quadratic Suffices for Over-parametrization via Matrix Chernoff Bound
Zhao Song, Xin Yang
We improve the over-parametrization size over two beautiful results [Li and Liang' 2018] and [Du, Zhai, Poczos and Singh' 2019] in deep learning theory.
Towards a Theoretical Understanding of Hashing-Based Neural Nets
Yibo Lin, Zhao Song, Lin F. Yang
Parameter reduction has been an important topic in deep learning due to the ever-increasing size of deep neural network models and the need to train and run them on resource limite…