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Found 10 papers, 1 papers with code
eQTL Studies: from Bulk Tissues to Single Cells
An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of certain genes that can be both nearby or distant.
Multi-task Learning for Gaussian Graphical Regressions with High Dimensional Covariates
Gaussian graphical regression is a powerful means that regresses the precision matrix of a Gaussian graphical model on covariates, permitting the numbers of the response variables and covariates to far exceed the sample size.
Discriminative-Generative Representation Learning for One-Class Anomaly Detection
As a kind of generative self-supervised learning methods, generative adversarial nets have been widely studied in the field of anomaly detection.
Jointly Modeling and Clustering Tensors in High Dimensions
We consider the problem of jointly modeling and clustering populations of tensors by introducing a high-dimensional tensor mixture model with heterogeneous covariances.
Limiting laws and consistent estimation criteria for fixed and diverging number of spiked eigenvalues
Firstly, for fixed $p$, we propose a generalized estimation criterion that can consistently estimate, $k$, the number of spiked eigenvalues.
Statistics Theory Methodology Statistics Theory
Fast Network Community Detection with Profile-Pseudo Likelihood Methods
In this article, we propose a novel likelihood based approach that decouples row and column labels in the likelihood function, which enables a fast alternating maximization; the new method is computationally efficient, performs well for both small and large scale networks, and has provable convergence guarantee.
Stochastic Low-rank Tensor Bandits for Multi-dimensional Online Decision Making
We consider two settings, tensor bandits without context and tensor bandits with context.
Latent Network Structure Learning from High Dimensional Multivariate Point Processes
We estimate the latent network structure using an efficient sparse least squares estimation approach.
Partially Observed Dynamic Tensor Response Regression
In this article, we develop a regression model with partially observed dynamic tensor as the response and external covariates as the predictor.
Sparse Tensor Additive Regression
Tensors are becoming prevalent in modern applications such as medical imaging and digital marketing.
