45 citations · 146 across the 15 of their papers we have counts for
15 papers
Understanding Intrinsic Robustness Using Label Uncertainty
Xiao Zhang, David Evans
A fundamental question in adversarial machine learning is whether a robust classifier exists for a given task. A line of research has made some progress towards this goal by studyi…
Improved Estimation of Concentration Under -Norm Distance Metrics Using Half Spaces
Jack Prescott, Xiao Zhang, David Evans
Concentration of measure has been argued to be the fundamental cause of adversarial vulnerability. Mahloujifar et al. presented an empirical way to measure the concentration of a d…
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models
Xiao Zhang, Jinghui Chen, Quanquan Gu +1
Starting with Gilmer et al. (2018), several works have demonstrated the inevitability of adversarial examples based on different assumptions about the underlying input probability…
Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization
Sicheng Zhu, Xiao Zhang, David Evans
Training machine learning models that are robust against adversarial inputs poses seemingly insurmountable challenges. To better understand adversarial robustness, we consider the…
Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness
Saeed Mahloujifar, Xiao Zhang, Mohammad Mahmoody +1
Many recent works have shown that adversarial examples that fool classifiers can be found by minimally perturbing a normal input. Recent theoretical results, starting with Gilmer e…
Cost-Sensitive Robustness against Adversarial Examples
Xiao Zhang, David Evans
Several recent works have developed methods for training classifiers that are certifiably robust against norm-bounded adversarial perturbations. These methods assume that all the a…
Learning One-hidden-layer ReLU Networks via Gradient Descent
Xiao Zhang, Yaodong Yu, Lingxiao Wang +1
We study the problem of learning one-hidden-layer neural networks with Rectified Linear Unit (ReLU) activation function, where the inputs are sampled from standard Gaussian distrib…
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow
Xiao Zhang, Simon S. Du, Quanquan Gu
We revisit the inductive matrix completion problem that aims to recover a rank- matrix with ambient dimension given features as the side prior information. The goal is t…
Entanglement-guided architectures of machine learning by quantum tensor network
Yuhan Liu, Xiao Zhang, Maciej Lewenstein +1
It is a fundamental, but still elusive question whether the schemes based on quantum mechanics, in particular on quantum entanglement, can be used for classical information process…
Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption
Jinghui Chen, Lingxiao Wang, Xiao Zhang +1
We consider the robust phase retrieval problem of recovering the unknown signal from the magnitude-only measurements, where the measurements can be contaminated by both sparse arbi…
A Unified Framework for Low-Rank plus Sparse Matrix Recovery
Xiao Zhang, Lingxiao Wang, Quanquan Gu
We propose a unified framework to solve general low-rank plus sparse matrix recovery problems based on matrix factorization, which covers a broad family of objective functions sati…
A Universal Variance Reduction-Based Catalyst for Nonconvex Low-Rank Matrix Recovery
Lingxiao Wang, Xiao Zhang, Quanquan Gu
We propose a generic framework based on a new stochastic variance-reduced gradient descent algorithm for accelerating nonconvex low-rank matrix recovery. Starting from an appropria…
Stochastic Variance-reduced Gradient Descent for Low-rank Matrix Recovery from Linear Measurements
Xiao Zhang, Lingxiao Wang, Quanquan Gu
We study the problem of estimating low-rank matrices from linear measurements (a.k.a., matrix sensing) through nonconvex optimization. We propose an efficient stochastic variance r…
A Unified Computational and Statistical Framework for Nonconvex Low-Rank Matrix Estimation
Lingxiao Wang, Xiao Zhang, Quanquan Gu
We propose a unified framework for estimating low-rank matrices through nonconvex optimization based on gradient descent algorithm. Our framework is quite general and can be applie…
Rademacher Complexity of the Restricted Boltzmann Machine
Xiao Zhang
Boltzmann machine, as a fundamental construction block of deep belief network and deep Boltzmann machines, is widely used in deep learning community and great success has been achi…