3 citations · 7 across the 16 of their papers we have counts for
17 papers
Personalized Federated Vector Autoregression with Personalization Diversity
Zhiyun Fan, Xiaoyu Zhang, Guodong Li +1
Personalized federated learning can improve estimation for high-dimensional time series while preserving client-specific dynamics. However, parameter heterogeneity creates a fundam…
Mixed-Frequency Time Series Forecasting via Depth-Separable Neural Networks
Yize Wang, Qianqian Zhu, Guodong Li
To better forecast mixed-frequency time series, it is the key to choose a suitable way for frequency alignment. However, the existing methods are all limited to linear transformati…
ParaRNN: An Interpretable and Parallelizable Recurrent Neural Network for Time-Dependent Data
Yuxi Cai, Lan Li, Feiqing Huang +1
The proliferation of large-scale and structurally complex data has spurred the integration of machine learning methods into statistical modeling. Recurrent neural networks (RNNs),…
Reduced-Rank Network Autoregression with Grouped Edge Effects
Qi Lyu, Xiaoyu Zhang, Guodong Li +1
We propose the Edge-Grouped Reduced-Rank Network Autoregressive model (Edge-Grouped RRNAR) for multivariate time series observed over a network. The model allows transmission effec…
High-dimensional Autoregressive Modeling for Time Series with Hierarchical Structures
Lan Li, Shibo Yu, Yingzhou Wang +1
Modern applications have made ubiquitous high-dimensional data, especially time-dependent data, with more and more complicated structures, and it also has become more frequent to e…
Improving time series estimation and prediction via transfer learning
Yuchang Lin, Qianqian Zhu, Guodong Li
There are many time series in the literature with high dimension yet limited sample sizes, such as macroeconomic variables, and it is almost impossible to obtain efficient estimati…
High-dimensional low-rank matrix regression with unknown latent structures
Di Wang, Xiaoyu Zhang, Guodong Li +1
We study low-rank matrix regression in settings where matrix-valued predictors and scalar responses are observed across multiple individuals. Rather than assuming a fully homogeneo…
An Efficient and Interpretable Autoregressive Model for High-Dimensional Tensor-Valued Time Series
Yuxi Cai, Lan Li, Yize Wang +1
In autoregressive modeling for tensor-valued time series, Tucker decomposition, when applied to the coefficient tensor, provides a clear interpretation of supervised factor modelin…
Robust Gradient Descent Estimation for Tensor Models under Heavy-Tailed Distributions
Xiaoyu Zhang, Di Wang, Guodong Li +1
Low-rank tensor models are widely used in statistics. However, most existing methods rely heavily on the assumption that data follows a sub-Gaussian distribution. To address the ch…
Robust estimation for high-dimensional time series with heavy tails
Yu Wang, Guodong Li, Zhijie Xiao +2
We study in this paper the problem of least absolute deviation (LAD) regression for high-dimensional heavy-tailed time series which have finite -th moment with . To…
A computationally efficient multivariate volatility model for many assets
Wenyu Li, Yuchang Lin, Qianqian Zhu +1
This paper develops a flexible and computationally efficient multivariate volatility model that accommodates dynamic conditional correlations and volatility spillover effects among…
Supervised Factor Modeling for High-Dimensional Linear Time Series
Feiqing Huang, Kexin Lu, Guodong Li
Motivated by Tucker tensor decomposition, this paper imposes low-rank structures to the column and row spaces of coefficient matrices in a multivariate infinite-order vector autore…
Tucker tensor factor models: matricization and mode-wise PCA estimation
Xu Zhang, Guodong Li, Catherine C. Liu +1
High-dimensional, higher-order tensor data are gaining prominence in a variety of fields, including but not limited to computer vision and network analysis. Tensor factor models, i…
An efficient tensor regression for high-dimensional data
Yuefeng Si, Yingying Zhang, Yuxi Cai +2
Most currently used tensor regression models for high-dimensional data are based on Tucker decomposition, which has good properties but loses its efficiency in compressing tensors…
A New Measure of Model Redundancy for Compressed Convolutional Neural Networks
Feiqing Huang, Yuefeng Si, Yao Zheng +1
While recently many designs have been proposed to improve the model efficiency of convolutional neural networks (CNNs) on a fixed resource budget, theoretical understanding of thes…
Quantile index regression
Yingying Zhang, Yuefeng Si, Guodong Li +1
Estimating the structures at high or low quantiles has become an important subject and attracted increasing attention across numerous fields. However, due to data sparsity at tails…
An Asymptotic Analysis of Minibatch-Based Momentum Methods for Linear Regression Models
Yuan Gao, Xuening Zhu, Haobo Qi +3
Momentum methods have been shown to accelerate the convergence of the standard gradient descent algorithm in practice and theory. In particular, the minibatch-based gradient descen…