1 citations · 1 across the 8 of their papers we have counts for
9 papers
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…
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…
Detection Is Harder Than Estimation in Certain Regimes: Inference for Moment and Cumulant Tensors
Runshi Tang, Yuefeng Han, Anru R. Zhang
We study estimation and detection of high-order cumulant tensors from i.i.d. observations of a -dimensional random vector, with performance measured in the tensor spectral n…
From Reverse Detection--Estimation Gaps to Computational Lower Bounds for Norm Approximation
Runshi Tang, Yuefeng Han, Anru R. Zhang
We develop a general reduction scheme that converts reverse detection--estimation gaps into computational lower bounds for norm approximation. If an efficiently computable estimato…
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…
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…
Tensor Decomposition with Unaligned Observations
Runshi Tang, Tamara Kolda, Anru R. Zhang
This paper presents a canonical polyadic (CP) tensor decomposition that addresses unaligned observations. The mode with unaligned observations is represented using functions in a r…
Cocaine Use Prediction with Tensor-based Machine Learning on Multimodal MRI Connectome Data
Anru R. Zhang, Ryan P. Bell, Chen An +5
This paper considers the use of machine learning algorithms for predicting cocaine use based on magnetic resonance imaging (MRI) connectomic data. The study utilized functional MRI…
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…