4 citations · 4 across the 6 of their papers we have counts for
7 papers
A Probabilistic Model for Zero-Inflated Count Tensors with Structured Latent Representations
Elena Tuzhilina, Yaoming Zhen
We propose a unified probabilistic framework for modeling high-dimensional count tensors with excess zeros. Such data arise naturally in a variety of applications, including single…
Sparse covariate-driven factorization of high-dimensional brain connectivity with application to site effect correction
Rongqian Zhang, Elena Tuzhilina, Jun Young Park
Large-scale neuroimaging studies often collect data from multiple scanners across different sites, where variations in scanners, scanning procedures, and other conditions across si…
Efficient Canonical Correlation Analysis with Sparsity
Zixuan Wu, Elena Tuzhilina, Coralie Rousseau +1
In high-dimensional settings, Canonical Correlation Analysis (CCA) often fails, and existing sparse methods force an untenable choice between computational speed and statistical ri…
Canonical Correlation Analysis as Reduced Rank Regression in High Dimensions
Claire Donnat, Elena Tuzhilina
Canonical correlation analysis is a widespread technique for discovering linear relationships between two sets of variables. In high dimensions, however, standard estimates of the…
Smooth multi-period forecasting with application to prediction of COVID-19 cases
Elena Tuzhilina, Trevor J. Hastie, Daniel J. McDonald +2
Forecasting methodologies have always attracted a lot of attention and have become an especially hot topic since the beginning of the COVID-19 pandemic. In this paper we consider t…
Weighted Low-Rank Matrix Approximation: Acceleration and Applications
Elena Tuzhilina, Trevor Hastie
Weighted low-rank matrix approximation (WLRMA) generalizes classical low-rank approximation and matrix completion by allowing arbitrary elementwise weights. Such formulations arise…