# A linear adjustment based approach to posterior drift in transfer learning

arXiv: https://arxiv.org/abs/2111.10841 · HTML: https://mlsanity.com/paper/2111.10841/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2021.

## Built on by (most cited first, 17 of 17)

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- [An adaptive transfer learning perspective on classification in non-stationary environments](https://mlsanity.com/paper/2405.18091/) (2024)
- [Multi-Task Learning with Covariate-Overlap Regularization](https://mlsanity.com/paper/2505.24281/) (2025)
- [Diagnosis-based mortality prediction for intensive care unit patients via transfer learning](https://mlsanity.com/paper/2512.06511/) (2025)
- [Robust inference for risk heterogeneity under group imbalance](https://mlsanity.com/paper/2606.00797/) (2026)
- [Retrieval-Based In-Context Learning: A Domain Adaptation Framework](https://mlsanity.com/paper/2610.05717/) (2026)

## Abstract (arXiv)

We present a new model and methods for the posterior drift problem where the regression function in the target domain is modeled as a linear adjustment (on an appropriate scale) of that in the source domain, an idea that inherits the simplicity and the usefulness of generalized linear models and accelerated failure time models from the classical statistics literature, and study the theoretical properties of our proposed estimator in the binary classification problem. Our approach is shown to be flexible and applicable in a variety of statistical settings, and can be adopted to transfer learning problems in various domains including epidemiology, genetics and biomedicine. As a concrete application, we illustrate the power of our approach through mortality prediction for British Asians by borrowing strength from similar data from the larger pool of British Caucasians, using the UK Biobank data.