2 citations · 4 across the 4 of their papers we have counts for
12 papers
Analysis of Relaxation Methods for Feature Selection in Mixed Effects Models
Aleksandr Aravkin, James Burke, Aleksei Sholokhov +1
Linear Mixed-Effects (LME) models are a fundamental tool for modeling clustered data, including cohort studies, longitudinal data analysis, and meta-analysis. The design and analys…
A Relaxation Approach to Feature Selection for Linear Mixed Effects Models
Aleksei Sholokhov, James V. Burke, Damian F. Santomauro +2
Linear Mixed-Effects (LME) models are a fundamental tool for modeling correlated data, including cohort studies, longitudinal data analysis, and meta-analysis. Design and analysis…
Efficient Robust Parameter Identification in Generalized Kalman Smoothing Models
Jonathan Jonker, Peng Zheng, Aleksandr Y. Aravkin
Dynamic inference problems in autoregressive (AR/ARMA/ARIMA), exponential smoothing, and navigation are often formulated and solved using state-space models (SSM), which allow a ra…
Trimmed Constrained Mixed Effects Models: Formulations and Algorithms
Peng Zheng, Ryan Barber, Reed J. D. Sorensen +2
Mixed effects (ME) models inform a vast array of problems in the physical and social sciences, and are pervasive in meta-analysis. We consider ME models where the random effects co…
A unified sparse optimization framework to learn parsimonious physics-informed models from data
Kathleen Champion, Peng Zheng, Aleksandr Y. Aravkin +2
Machine learning (ML) is redefining what is possible in data-intensive fields of science and engineering. However, applying ML to problems in the physical sciences comes with a uni…
Computer Assisted Localization of a Heart Arrhythmia
Chris Vogl, Peng Zheng, Stephen P. Seslar +1
We consider the problem of locating a point-source heart arrhythmia using data from a standard diagnostic procedure, where a reference catheter is placed in the heart, and arrival…