1 citations · 2 across the 5 of their papers we have counts for
5 papers
Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies
Matthew Lau, Tian-Yi Zhou, Xiangchi Yuan +3
Anomaly detection (AD) is a critical task across domains such as cybersecurity and healthcare. In the unsupervised setting, an effective and theoretically-grounded principle is to…
Learning to Detect Cyber Attacks: Neural Anomaly Detection for Cybersecurity with Theoretical Insights
Tian-Yi Zhou, Matthew Lau, Jizhou Chen +2
In cybersecurity practice, new forms of cyberattacks continuously emerge, deliberately designed to evade defense systems that rely on previously observed behaviors. Motivated by th…
Neural Operators for Nonlinear Functionals on RKHS
Tian-Yi Zhou, Namjoon Suh, Guang Cheng +1
Motivated by the abundance of functional data, such as time series and images, we study the approximation and statistical learning of nonlinear functionals defined on reproducing k…
Classification of Data Generated by Gaussian Mixture Models Using Deep ReLU Networks
Tian-Yi Zhou, Xiaoming Huo
This paper studies the binary classification of unbounded data from generated under Gaussian Mixture Models (GMMs) using deep ReLU neural networks. We obtain $\unic…
Learning Ability of Interpolating Deep Convolutional Neural Networks
Tian-Yi Zhou, Xiaoming Huo
It is frequently observed that overparameterized neural networks generalize well. Regarding such phenomena, existing theoretical work mainly devotes to linear settings or fully-con…