no code implementations • 8 Apr 2023 • Chanwoo Lee, Miaoyan Wang
We find that high-dimensional latent variable tensors are of log-rank; the fact explains the pervasiveness of low-rank tensors in applications.
no code implementations • 19 Jan 2022 • Jiaxin Hu, Miaoyan Wang
We consider the problem of multiway clustering in the presence of unknown degree heterogeneity.
no code implementations • 8 Nov 2021 • Chanwoo Lee, Miaoyan Wang
A phase transition phenomenon is revealed with respect to the smoothness threshold needed for optimal recovery.
no code implementations • 4 May 2021 • Chanwoo Lee, Lexin Li, Hao Helen Zhang, Miaoyan Wang
Trace regression is a widely used method to model effects of matrix predictors and has shown great success in matrix learning.
no code implementations • NeurIPS 2021 • Chanwoo Lee, Miaoyan Wang
A nonparametric approach to tensor completion is developed based on a new model which we coin as sign representable tensors.
1 code implementation • 18 Dec 2020 • Rungang Han, Yuetian Luo, Miaoyan Wang, Anru R. Zhang
High-order clustering aims to identify heterogeneous substructures in multiway datasets that arise commonly in neuroimaging, genomics, social network studies, etc.
no code implementations • ICML 2020 • Chanwoo Lee, Miaoyan Wang
Higher-order tensors arise frequently in applications such as neuroimaging, recommendation system, social network analysis, and psychological studies.
no code implementations • 21 Oct 2019 • Jiaxin Hu, Chanwoo Lee, Miaoyan Wang
Here, we develop a tensor decomposition method that incorporates multiple feature matrices as side information.
no code implementations • NeurIPS 2019 • Miaoyan Wang, Yuchen Zeng
We consider the problem of identifying multiway block structure from a large noisy tensor.
1 code implementation • 13 Nov 2018 • Miaoyan Wang, Lexin Li
We consider the problem of decomposing a higher-order tensor with binary entries.
no code implementations • 12 Dec 2016 • Miaoyan Wang, Yun S. Song
Tensor decompositions have rich applications in statistics and machine learning, and developing efficient, accurate algorithms for the problem has received much attention recently.