4 citations · 5 across the 6 of their papers we have counts for
7 papers
Variable Importance Identification Through Lazy Training for Binary Classification
Anand Singh, Luke Pennella, Eshan Kabir +1
Deep neural networks have been widely used in many applications (e.g., computer vision and natural language processing); however, understanding their explainability remains a chall…
Statistical Guarantees for Distributionally Robust Optimization with Optimal Transport and OT-Regularized Divergences
Jeremiah Birrell, Xiaoxi Shen
We study finite-sample statistical performance guarantees for distributionally robust optimization (DRO) with optimal transport (OT) and OT-regularized divergence model neighborhoo…
A Sieve Quasi-likelihood Ratio Test for Neural Networks with Applications to Genetic Association Studies
Xiaoxi Shen, Chang Jiang, Lyudmila Sakhanenko +1
Neural networks (NN) play a central role in modern Artificial intelligence (AI) technology and has been successfully used in areas such as natural language processing and image rec…
Consistency of Neural Networks with Regularization
Xiaoxi Shen, Jinghang Lin
Neural networks have attracted a lot of attention due to its success in applications such as natural language processing and computer vision. For large scale data, due to the treme…
A Kernel-Based Neural Network for High-dimensional Genetic Risk Prediction Analysis
Xiaoxi Shen, Xiaoran Tong, Qing Lu
Risk prediction capitalizing on emerging human genome findings holds great promise for new prediction and prevention strategies. While the large amounts of genetic data generated f…
Asymptotic Theory of Expectile Neural Networks
Jinghang Lin, Xiaoxi Shen, Qing Lu
Neural networks are becoming an increasingly important tool in applications. However, neural networks are not widely used in statistical genetics. In this paper, we propose a new n…
Asymptotic Properties of Neural Network Sieve Estimators
Xiaoxi Shen, Chang Jiang, Lyudmila Sakhanenko +1
Neural networks are one of the most popularly used methods in machine learning and artificial intelligence nowadays. Due to the universal approximation theorem (Hornik et al. (1989…