5 citations · 6 across the 9 of their papers we have counts for
11 papers
Knockoffs-based False Discovery Rate Control and Simplification for Deep Neural Networks
Wenyu Liao, Yiqing Shi, Fang Xie
The deep neural network is a widely used framework in machine learning that has been widely applied in various fields. However, deep neural networks often involve a large number of…
Revisiting Privacy Amplification by Subsampling in Selective Release DPSGD
Xiaobo Huang, Fang Xie
Machine learning's reliance on sensitive data necessitates privacy-preserving techniques like Differentially Private Stochastic Gradient Descent (DPSGD). However, DPSGD suffers fro…
When Do Fewer Coordinates Suffice in DP-SGD?
Huiqi Zhang, Fang Xie
Differentially private stochastic gradient descent (DP-SGD) injects noise into every updated coordinate, making the injected noise energy scale with the ambient parameter dimension…
Steps Adaptive Decay DPSGD: Enhancing Performance on Imbalanced Datasets with Differential Privacy with HAM10000
Xiaobo Huang, Fang Xie
When applying machine learning to medical image classification, data leakage is a critical issue. Previous methods, such as adding noise to gradients for differential privacy, work…
AdaDPIGU: Differentially Private SGD with Adaptive Clipping and Importance-Based Gradient Updates for Deep Neural Networks
Huiqi Zhang, Fang Xie
Differential privacy has been proven effective for stochastic gradient descent; however, existing methods often suffer from performance degradation in high-dimensional settings, as…
LH2Face: Loss function for Hard High-quality Face
Fan Xie, Yang Wang, Yikang Jiao +3
In current practical face authentication systems, most face recognition (FR) algorithms are based on cosine similarity with softmax classification. Despite its reliable classificat…
Deriving the Gradients of Some Popular Optimal Transport Algorithms
Fangzhou Xie
In this note, I review entropy-regularized Monge-Kantorovich problem in Optimal Transport, and derive the gradients of several popular algorithms popular in Computational Optimal T…
Statistical Guarantees for Approximate Stationary Points of Shallow Neural Networks
Mahsa Taheri, Fang Xie, Johannes Lederer
Since statistical guarantees for neural networks are usually restricted to global optima of intricate objective functions, it is unclear whether these theories explain the performa…
Statistical Guarantees for Regularized Neural Networks
Mahsa Taheri, Fang Xie, Johannes Lederer
Neural networks have become standard tools in the analysis of data, but they lack comprehensive mathematical theories. For example, there are very few statistical guarantees for le…
Tuning-free ridge estimators for high-dimensional generalized linear models
Shih-Ting Huang, Fang Xie, Johannes Lederer
Ridge estimators regularize the squared Euclidean lengths of parameters. Such estimators are mathematically and computationally attractive but involve tuning parameters that can be…
Aggregating Knockoffs for False Discovery Rate Control with an Application to Gut Microbiome Data
Fang Xie, Johannes Lederer
Recent discoveries suggest that our gut microbiome plays an important role in our health and wellbeing. However, the gut microbiome data are intricate; for example, the microbial d…