Motivation Most gene prioritization methods model each disease or phenotype individually,
but this fails to capture patterns common to several diseases or phenotypes. To overcome …

Multiway data analysis deals with multiway arrays, ie, tensors, and the goal is twofold:
predicting missing entries by modeling the interactions between array elements and …

Bayesian matrix factorization is a method of choice for making predictions for large-scale
incomplete matrices, due to availability of efficient Gibbs sampling schemes and its …

Multiway data are widely observed in neuroscience, health informatics, food science, etc.
Tensor decomposition is an important technique for capturing high-order interactions among …

Tensor factorization methods provide a useful way to extract latent factors from complex
multirelational data, and also for predicting missing data. Develo** tensor factorization …

We present a scalable Bayesian model for low-rank factorization of massive tensors with
binary observations. The proposed model has the following key properties:(1) in contrast to …

We consider the problem of low-rank decomposition of incomplete tensors. Since many real-
world data lie on an intrinsically low-dimensional subspace, tensor low-rank decomposition …

By restricting the iterate on a nonlinear manifold, the recently proposed Riemannian
optimization methods prove to be both efficient and effective in low rank tensor completion …

Dynamic modeling of longitudinal networks has been an increasingly important topic in
applied research. While longitudinal network data commonly exhibit dramatic changes in its …

A continuous-time tensor factorization method is developed for event sequences containing
multiple “modalities.” Each data element is a point in a tensor, whose dimensions are …