5 papers · 1 filter
Bandable Cumulant Tensors: Optimal Estimation and Applications in Non-Gaussian Data Modeling
Runshi Tang, Anru R. Zhang, Yuefeng Han +1
Higher-order cumulants capture the non-Gaussian dependence that covariance misses, but they are hard to use in high dimensions. An order- cumulant tensor has entries, and…
A functional tensor model for dynamic multilayer networks with common invariant subspaces and the RKHS estimation
Runshi Tang, Runbing Zheng, Anru R. Zhang +1
Dynamic multilayer networks are frequently used to describe the structure and temporal evolution of multiple relationships among common entities, with applications in fields such a…
Optimal Estimation of Discrete Multiview Distributions under Heteroskedastic Multinomial Sampling
Runshi Tang, Julien Chhor, Olga Klopp +2
Multiview latent-variable models provide a fundamental framework for discrete data analysis, with applications to latent structure models, topic models, and mixtures of product dis…
Revisit CP Tensor Decomposition: Statistical Optimality and Fast Convergence
Runshi Tang, Julien Chhor, Olga Klopp +1
Canonical Polyadic (CP) tensor decomposition is a fundamental technique for analyzing high-dimensional tensor data. While the Alternating Least Squares (ALS) algorithm is widely us…
Mode-wise Principal Subspace Pursuit and Matrix Spiked Covariance Model
Runshi Tang, Ming Yuan, Anru R. Zhang
This paper introduces a novel framework called Mode-wise Principal Subspace Pursuit (MOP-UP) to extract hidden variations in both the row and column dimensions for matrix data. To…