9 papers
Learning Perturbations to Extrapolate Your LLM
Zetai Cen, Chenfei Gu, Jin Zhu +3
Recent advancements in large language models demonstrate that injecting perturbations can substantially enhance extrapolation performance. However, current approaches often rely on…
Perturbation is All You Need for Extrapolating Language Models
Zetai Cen, Jin Zhu, Xinwei Shen +1
This paper develops a statistical theory of extrapolation for large language models, by reinterpreting them through pre-post-additive noise models. In contrast to the standard auto…
Detection and Mode-Identification of Multiple Change Points in Tensor Factor Models
Yuqi Zhang, Zetai Cen, Haeran Cho
We study the problems arising from modeling high-dimensional tensor-valued time series under a Tucker decomposition-based factor model with multiple structural change points. First…
Identification and Estimation of Multi-order Tensor Factor Models
Zetai Cen
We propose a novel framework in high-dimensional factor models to simultaneously analyse multiple tensor time series, each with potentially different tensor orders and dimensionali…
Main Effect Factor Models in High-Dimensional Matrix Time Series: Identification and Sparsity
Zetai Cen, Kaixin Liu, Clifford Lam
We propose a general identification framework for main effect factor models for matrix-valued time series. The classical sum-to-zero restriction on the row and column main effects…
Inference on Dynamic Spatial Autoregressive Models with Change Point Detection
Zetai Cen, Yudong Chen, Clifford Lam
We analyze a varying-coefficient dynamic spatial autoregressive model with spatial fixed effects. One salient feature of the model is the incorporation of multiple spatial weight m…