# A Theory of Multiple-Source Adaptation with Limited Target Labeled Data

arXiv: https://arxiv.org/abs/2007.09762 · HTML: https://mlsanity.com/paper/2007.09762/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2020.

## Built on by (most cited first, 18 of 18)

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- [Best-Effort Adaptation](https://mlsanity.com/paper/2305.05816/) (2023)
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- [Algorithms and Theory for Supervised Gradual Domain Adaptation](https://mlsanity.com/paper/2204.11644/) (2022)
- [Probably Approximately Correct Federated Learning](https://mlsanity.com/paper/2304.04641/) (2023)
- [Wasserstein Distributionally Robust Optimization with Wasserstein Barycenters](https://mlsanity.com/paper/2203.12136/) (2022)
- [Robust Offline Policy Learning with Observational Data from Multiple Sources](https://mlsanity.com/paper/2410.08537/) (2024)
- [Collaborative Learning with Different Labeling Functions](https://mlsanity.com/paper/2402.10445/) (2024)
- [PAC Generalization via Invariant Representations](https://mlsanity.com/paper/2205.15196/) (2022)
- [Mixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptation](https://mlsanity.com/paper/2309.10736/) (2023)
- [Federated Offline Policy Learning](https://mlsanity.com/paper/2305.12407/) (2023)
- [Collaborative Multi-source Domain Adaptation Through Optimal Transport](https://mlsanity.com/paper/2404.06599/) (2024)
- [Guided Uncertainty-Aware Robust Domain Transfer](https://mlsanity.com/paper/2609.35654/) (2026)

## Abstract (arXiv)

We present a theoretical and algorithmic study of the multiple-source domain adaptation problem in the common scenario where the learner has access only to a limited amount of labeled target data, but where the learner has at disposal a large amount of labeled data from multiple source domains. We show that a new family of algorithms based on model selection ideas benefits from very favorable guarantees in this scenario and discuss some theoretical obstacles affecting some alternative techniques. We also report the results of several experiments with our algorithms that demonstrate their practical effectiveness.