64 citations · 192 across the 29 of their papers we have counts for
4 papers · 1 filter
Multi-Response Heteroscedastic Gaussian Process Models and Their Inference
Taehee Lee, Jun S. Liu
Despite the widespread utilization of Gaussian process models for versatile nonparametric modeling, they exhibit limitations in effectively capturing abrupt changes in function smo…
Neural Gaussian Mirror for Controlled Feature Selection in Neural Networks
Xin Xing, Yu Gui, Chenguang Dai +1
Deep neural networks (DNNs) have become increasingly popular and achieved outstanding performance in predictive tasks. However, the DNN framework itself cannot inform the user whic…
Measurement error models: from nonparametric methods to deep neural networks
Zhirui Hu, Zheng Tracy Ke, Jun S Liu
The success of deep learning has inspired recent interests in applying neural networks in statistical inference. In this paper, we investigate the use of deep neural networks for n…
Interpretable Selection and Visualization of Features and Interactions Using Bayesian Forests
Viktoriya Krakovna, Jiong Du, Jun S. Liu
It is becoming increasingly important for machine learning methods to make predictions that are interpretable as well as accurate. In many practical applications, it is of interest…