6 papers
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…
Functional Tensor Regression
Tongyu Li, Fang Yao, Anru R. Zhang
Tensor regression has attracted significant attention in statistical research. This study tackles the challenge of handling covariates with smooth varying structures. We introduce…
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…
High-order Accurate Inference on Manifolds
Chengzhu Huang, Anru R. Zhang
We present a new framework for statistical inference on Riemannian manifolds that achieves high-order accuracy, addressing the challenges posed by non-Euclidean parameter spaces fr…
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…
Tensor Decomposition Meets RKHS: Efficient Algorithms for Smooth and Misaligned Data
Brett W. Larsen, Tamara G. Kolda, Anru R. Zhang +1
The canonical polyadic (CP) tensor decomposition decomposes a multidimensional data array into a sum of outer products of finite-dimensional vectors. Instead, we can replace some o…