7 citations · 7 across the 4 of their papers we have counts for
4 papers · 1 filter
Minimax and Adaptive Transfer Learning for Sparse Canonical Correlation Analysis
Bo Fu, Dandan Jiang
We develop a transfer learning framework for high-dimensional sparse canonical correlation analysis (CCA) with multiple heterogeneous source datasets. Our goal is to improve estima…
Invariant quantile regression for heterogeneous environments
Bo Fu, Dandan Jiang
In this paper, we propose an invariant quantile regression (IQR) framework specifically designed for multi-environment datasets, which captures the invariance across different envi…
Unifiedly Efficient Inference on All-Dimensional Targets for Large-Scale GLMs
Bo Fu, Dandan Jiang
The scalability of Generalized Linear Models (GLMs) for large-scale, high-dimensional data often forces a trade-off between computational feasibility and statistical accuracy, part…
Transfer learning for high-dimensional Factor-augmented sparse linear model
Bo Fu, Dandan Jiang
In this paper, we study transfer learning for high-dimensional factor-augmented sparse linear models, motivated by applications in economics and finance where strongly correlated p…