19 citations · 45 across the 11 of their papers we have counts for
13 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…
A Unified Framework for Sparse Relaxed Regularized Regression: SR3
Peng Zheng, Travis Askham, Steven L. Brunton +2
Regularized regression problems are ubiquitous in statistical modeling, signal processing, and machine learning. Sparse regression in particular has been instrumental in scientific…
Learning Nonlinear Brain Dynamics: van der Pol Meets LSTM
German Abrevaya, Irina Rish, Aleksandr Y. Aravkin +7
Many real-world data sets, especially in biology, are produced by complex nonlinear dynamical systems. In this paper, we focus on brain calcium imaging (CaI) of different organisms…
M-estimation with the Trimmed l1 Penalty
Jihun Yun, Peng Zheng, Eunho Yang +2
We study high-dimensional estimators with the trimmed penalty, which leaves the largest parameter entries penalty-free. While optimization techniques for this nonconve…
Relax-and-split method for nonsmooth nonconvex problems
Peng Zheng, Aleksandr Aravkin
We develop and analyze a new `relax-and-split' (RS) approach for compositions of separable nonconvex nonsmooth functions with linear maps. RS uses a relaxation technique together w…
Robust and scalable methods for the dynamic mode decomposition
Travis Askham, Peng Zheng, Aleksandr Aravkin +1
The dynamic mode decomposition (DMD) is a broadly applicable dimensionality reduction algorithm that approximates a matrix containing time-series data by the outer product of a mat…
Learning Robust Representations for Computer Vision
Peng Zheng, Aleksandr Y. Aravkin, Karthikeyan Natesan Ramamurthy +1
Unsupervised learning techniques in computer vision often require learning latent representations, such as low-dimensional linear and non-linear subspaces. Noise and outliers in th…
Estimating Shape Parameters of Piecewise Linear-Quadratic Problems
Peng Zheng, Aleksandr Y. Aravkin, Karthikeyan Natesan Ramamurthy
Piecewise Linear-Quadratic (PLQ) penalties are widely used to develop models in statistical inference, signal processing, and machine learning. Common examples of PLQ penalties inc…