# Simultaneous Estimation and Dataset Selection for Transfer Learning in High Dimensions by a Non-convex Penalty

arXiv: https://arxiv.org/abs/2306.04182 · HTML: https://mlsanity.com/paper/2306.04182/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2023.

## Built on by (most cited first, 2 of 2)

- [Representational Transfer Learning for Matrix Completion](https://mlsanity.com/paper/2412.06233/) (2024)
- [Transfer Learning for Matrix Completion](https://mlsanity.com/paper/2507.02248/) (2025)

## Abstract (arXiv)

In this paper, we propose to estimate model parameters and identify informative source datasets simultaneously for high-dimensional transfer learning problems with the aid of a non-convex penalty, in contrast to the separate useful dataset selection and transfer learning procedures in the existing literature. To numerically solve the non-convex problem with respect to two specific statistical models, namely the sparse linear regression and the generalized low-rank trace regression models, we adopt the difference of convex (DC) programming with the alternating direction method of multipliers (ADMM) procedures. We theoretically justify the proposed algorithm from both statistical and computational perspectives. Extensive numerical results are reported alongside to validate the theoretical assertions. An textttR package textttMHDTL is developed to implement the proposed methods.