9 citations · 29 across the 7 of their papers we have counts for
8 papers
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…
A Scale-free Approach for False Discovery Rate Control in Generalized Linear Models
Chenguang Dai, Buyu Lin, Xin Xing +1
The generalized linear models (GLM) have been widely used in practice to model non-Gaussian response variables. When the number of explanatory features is relatively large, scienti…
An invitation to sequential Monte Carlo samplers
Chenguang Dai, Jeremy Heng, Pierre E. Jacob +1
Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo sample…
False Discovery Rate Control via Data Splitting
Chenguang Dai, Buyu Lin, Xin Xing +1
Selecting relevant features associated with a given response variable is an important issue in many scientific fields. Quantifying quality and uncertainty of a selection result via…
Late 19th-Century Navigational Uncertainties and Their Influence on Sea Surface Temperature Estimates
Chenguang Dai, Duo Chan, Peter Huybers +1
Accurate estimates of historical changes in sea surface temperatures (SSTs) and their uncertainties are important for documenting and understanding historical changes in climate. A…
Monte Carlo Approximation of Bayes Factors via Mixing with Surrogate Distributions
Chenguang Dai, Jun S. Liu
By mixing the target posterior distribution with a surrogate distribution, of which the normalizing constant is tractable, we propose a method for estimating the marginal likelihoo…
The Wang-Landau Algorithm as Stochastic Optimization and Its Acceleration
Chenguang Dai, Jun S. Liu
We show that the Wang-Landau algorithm can be formulated as a stochastic gradient descent algorithm minimizing a smooth and convex objective function, of which the gradient is esti…
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…