Higher-order multiway data is ubiquitous in machine learning and statist...
The transition kernel of a continuous-state-action Markov decision proce...
This paper describes a flexible framework for generalized low-rank tenso...
In this paper, we develop a novel procedure for low-rank tensor regressi...
In this paper, we study sparse group Lasso for high-dimensional double s...
Modeling unknown systems from data is a precursor of system optimization...
In microbiome and genomic study, the regression of compositional data ha...
The non-asymptotic tail bounds of random variables play crucial roles in...
The Chernoff-Cramèr bound is a widely used technique to analyze the uppe...
Principal component analysis (PCA) and singular value decomposition (SVD...
In this article, we consider the sparse tensor singular value decomposit...
This paper studies the recovery and state compression of low-rank Markov...
Model reduction is a central problem in analyzing complex systems and
hi...
In this paper, we propose a general framework for sparse and low-rank te...
Motivated by applications of mixed longitudinal studies, where a group o...
In this paper, we propose a general framework for tensor singular value
...
The completion of tensors, or high-order arrays, attracts significant
at...
We propose a general semi-supervised inference framework focused on the
...
Matrix completion has attracted significant recent attention in many fie...
This paper considers compressed sensing and affine rank minimization in ...