6 papers
Factor Augmented High-Dimensional SGD
Shubo Li, Yuefeng Han, Xiufan Yu
Stochastic gradient descent (SGD) is a fundamental optimization algorithm widely used in modern machine learning. In this paper, we propose Factor-Augmented SGD (FSGD), a new optim…
NetworkNet: A Deep Neural Network Approach for Random Networks with Sparse Nodal Attributes and Complex Nodal Heterogeneity
Zhaoyu Xing, Xiufan Yu
Heterogeneous network data with rich nodal information become increasingly prevalent across multidisciplinary research, yet accurately modeling complex nodal heterogeneity and simu…
Supervised Dynamic Dimension Reduction with Deep Neural Network
Zhanye Luo, Yuefeng Han, Xiufan Yu
This paper studies the problem of dimension reduction, tailored to improving time series forecasting with high-dimensional predictors. We propose a novel Supervised Deep Dynamic Pr…
Factor Augmented Supervised Learning with Text Embeddings
Zhanye Luo, Yuefeng Han, Xiufan Yu
Large language models (LLMs) generate text embeddings from text data, producing vector representations that capture the semantic meaning and contextual relationships of words. Howe…
Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models
Guanhao Zhou, Yuefeng Han, Xiufan Yu
This paper studies the task of estimating heterogeneous treatment effects in causal panel data models, in the presence of covariate effects. We propose a novel Covariate-Adjusted D…
Factor Augmented Tensor-on-Tensor Neural Networks
Guanhao Zhou, Yuefeng Han, Xiufan Yu
This paper studies the prediction task of tensor-on-tensor regression in which both covariates and responses are multi-dimensional arrays (a.k.a., tensors) across time with arbitra…