Least Absolute Shrinkage and Selection Operator with Transfer Learning

(Takada & Fujisawa, 2020, NeurIPS) proposed a method for transferring knowledge from a source domain to a target domain via $\ell_1$ regularization in high dimension. (Takada & Fujisawa, 2023, arXiv) extended the method to the weighted regularization to improve its theoretical properties. This is a simple implementation of the above methods.

Installation

To install the package:

# install.packages("remotes")
remotes::install_github("tkdmah/trlasso", build_vignettes = TRUE, dependencies = TRUE)

Usage

To learn how to use:

vignette("example_linear", package = "trlasso")
vignette("example_linear_weight", package = "trlasso")

File	Description
vignettes/example_linear.Rmd	Sample script for Transfer Lasso
vignettes/example_linear_weight.Rmd	Sample script for Weighted Transfer Lasso

Experiment Scripts

To replicate the experiments in (Takada & Fujisawa, 2023, arXiv):

File	Description
inst/experiments/empirical_convergence.Rmd	Experiment script for evaluating estimation errors with respect to sample size
inst/experiments/empirical_phase_diagram.Rmd	Experiment script for drawing empirical phase diagrams
inst/experiments/method_comparison_large.Rmd	Experiment script for comparing several methods using a large amount of source data (Note: time-consuming)
inst/experiments/method_comparison_medium.Rmd	Experiment script for comparing several methods using a medium amount of source data (Note: time-consuming)

References

1. Takada, M., & Fujisawa, H. (2020). Transfer Learning via $\ell_1$ Regularization. Advances in Neural Information Processing Systems, 33, 14266-14277.
2. Takada, M., & Fujisawa, H. (2023). Adaptive Lasso, Transfer Lasso, and Beyond: An Asymptotic Perspective. arXiv preprint arXiv:2308.15838.

Developers

• Masaaki Takada (Toshiba Corporation)
• Gen Li (Toshiba Corporation)
• Sunao Yotsutsuji (Toshiba Corporation)