research
∙
01/16/2023
A joint model of the individual mean and within-subject variability of a longitudinal outcome with a competing risks time-to-event outcome
Motivated by recent findings that within-subject (WS) variability of lon...
share
research
∙
05/15/2022
Proximal MCMC for Bayesian Inference of Constrained and Regularized Estimation
This paper advocates proximal Markov Chain Monte Carlo (ProxMCMC) as a f...
research
∙
05/06/2022
A Flexible Quasi-Copula Distribution for Statistical Modeling
Copulas, generalized estimating equations, and generalized linear mixed ...
research
∙
01/01/2022
Bayesian Trend Filtering via Proximal Markov Chain Monte Carlo
Proximal Markov Chain Monte Carlo is a novel construct that lies at the ...
research
∙
10/28/2021
Efficient Algorithms and Implementation of a Semiparametric Joint Model for Longitudinal and Competing Risks Data: With Applications to Massive Biobank Data
Semiparametric joint models of longitudinal and competing risks data are...
research
∙
08/10/2021
Bayesian Inference using the Proximal Mapping: Uncertainty Quantification under Varying Dimensionality
In statistical applications, it is common to encounter parameters suppor...
research
∙
06/05/2021
Nonconvex Optimization via MM Algorithms: Convergence Theory
The majorization-minimization (MM) principle is an extremely general fra...
research
∙
10/30/2020
DistStat.jl: Towards Unified Programming for High-Performance Statistical Computing Environments in Julia
The demand for high-performance computing (HPC) is ever-increasing for e...
research
∙
09/02/2020
Extensions to the Proximal Distance of Method of Constrained Optimization
The current paper studies the problem of minimizing a loss f(x) subject ...
research
∙
01/07/2020
High-Performance Statistical Computing in the Computing Environments of the 2020s
Technological advances in the past decade, hardware and software alike, ...
research
∙
06/25/2019
Probabilistic Error Analysis for Inner Products
Probabilistic models are proposed for bounding the forward error in the ...
research
∙
05/08/2019
2-bit Model Compression of Deep Convolutional Neural Network on ASIC Engine for Image Retrieval
Image retrieval utilizes image descriptors to retrieve the most similar ...
research
∙
02/14/2019
OPENMENDEL: A Cooperative Programming Project for Statistical Genetics
Statistical methods for genomewide association studies (GWAS) continue t...
research
∙
11/08/2018
Orthogonal Trace-Sum Maximization: Applications, Local Algorithms, and Global Optimality
This paper studies a problem of maximizing the sum of traces of matrix q...
research
∙
09/24/2018
Matrix Linear Discriminant Analysis
We propose a novel linear discriminant analysis approach for the classif...
research
∙
03/17/2018
Provable Convex Co-clustering of Tensors
Cluster analysis is a fundamental tool for pattern discovery of complex ...
research
∙
02/07/2018
MiMatrix: A Massively Distributed Deep Learning Framework on a Petascale High-density Heterogeneous Cluster
In this paper, we present a co-designed petascale high-density GPU clust...
research
∙
11/13/2017
MM Algorithms for Variance Component Estimation and Selection in Logistic Linear Mixed Model
Logistic linear mixed model is widely used in experimental designs and g...
research
∙
05/08/2017
ChineseFoodNet: A large-scale Image Dataset for Chinese Food Recognition
In this paper, we introduce a new and challenging large-scale food image...
research
∙
10/28/2016
Algorithms for Fitting the Constrained Lasso
We compare alternative computing strategies for solving the constrained ...
research
∙
11/16/2012