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stat.ME2025
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…
stat.ME2025
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…
stat.CO2025
Accelerating Randomized Algorithms for Low-Rank Matrix Approximation
Dandan Jiang, Bo Fu, Weiwei Xu
Randomized algorithms are overwhelming methods for low-rank approximation that can alleviate the computational expenditure with great reliability compared to deterministic algorith…