# Adversarial Weighting for Domain Adaptation in Regression

arXiv: https://arxiv.org/abs/2006.08251 · HTML: https://mlsanity.com/paper/2006.08251/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 19 of 19)

- [DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices](https://mlsanity.com/paper/2303.13325/) (2023)
- [A Short Survey on Importance Weighting for Machine Learning](https://mlsanity.com/paper/2403.10175/) (2024)
- [Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts](https://mlsanity.com/paper/2106.00322/) (2021)
- [ADAPT : Awesome Domain Adaptation Python Toolbox](https://mlsanity.com/paper/2107.03049/) (2021)
- [Domain Adaptation via Alignment of Operation Profile for Remaining Useful Lifetime Prediction](https://mlsanity.com/paper/2302.01704/) (2023)
- [InfAnFace: Bridging the infant-adult domain gap in facial landmark estimation in the wild](https://mlsanity.com/paper/2110.08935/) (2021)
- [Unsupervised Domain Adaptation for Constraining Star Formation Histories](https://mlsanity.com/paper/2112.14072/) (2021)
- [Domain-Adaptive Neural Posterior Estimation for Strong Gravitational Lens Analysis](https://mlsanity.com/paper/2410.16347/) (2024)
- [Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification](https://mlsanity.com/paper/2304.04042/) (2023)
- [Fast and Accurate Importance Weighting for Correcting Sample Bias](https://mlsanity.com/paper/2209.04215/) (2022)
- [COD: Learning Conditional Invariant Representation for Domain Adaptation Regression](https://mlsanity.com/paper/2408.06638/) (2024)
- [UDAMA: Unsupervised Domain Adaptation through Multi-discriminator Adversarial Training with Noisy Labels Improves Cardio-fitness Prediction](https://mlsanity.com/paper/2307.16651/) (2023)
- [Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest Monitoring](https://mlsanity.com/paper/2405.00514/) (2024)
- ["Forgetting" in Machine Learning and Beyond: A Survey](https://mlsanity.com/paper/2405.20620/) (2024)
- [Revisiting the Robustness of the Minimum Error Entropy Criterion: A Transfer Learning Case Study](https://mlsanity.com/paper/2307.08572/) (2023)
- [Weighted Unsupervised Domain Adaptation Considering Geometry Features and Engineering Performance of 3D Design Data](https://mlsanity.com/paper/2309.04499/) (2023)
- [Domain Adaptation Framework for Turning Movement Count Estimation with Limited Data](https://mlsanity.com/paper/2503.20113/) (2025)
- [Turning Silver into Gold: Domain Adaptation with Noisy Labels for Wearable Cardio-Respiratory Fitness Prediction](https://mlsanity.com/paper/2211.10475/) (2022)
- [Energy-Efficient Prediction in Textile Manufacturing: Enhancing Accuracy and Data Efficiency With Ensemble Deep Transfer Learning](https://mlsanity.com/paper/2601.12663/) (2026)

## Abstract (arXiv)

We present a novel instance-based approach to handle regression tasks in the context of supervised domain adaptation under an assumption of covariate shift. The approach developed in this paper is based on the assumption that the task on the target domain can be efficiently learned by adequately reweighting the source instances during training phase. We introduce a novel formulation of the optimization objective for domain adaptation which relies on a discrepancy distance characterizing the difference between domains according to a specific task and a class of hypotheses. To solve this problem, we develop an adversarial network algorithm which learns both the source weighting scheme and the task in one feed-forward gradient descent. We provide numerical evidence of the relevance of the method on public data sets for regression domain adaptation through reproducible experiments.