# On the impact of measure pre-conditionings on general parametric ML models and transfer learning via domain adaptation

arXiv: https://arxiv.org/abs/2403.02432 · HTML: https://mlsanity.com/paper/2403.02432/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Computational Optimal Transport](https://mlsanity.com/paper/1803.00567/) (2018)
- [CO-Optimal Transport](https://mlsanity.com/paper/2002.03731/) (2020)
- [Meta Optimal Transport](https://mlsanity.com/paper/2206.05262/) (2022)
- [Optimal Transport for Conditional Domain Matching and Label Shift](https://mlsanity.com/paper/2006.08161/) (2020)

## Abstract (arXiv)

We study a new technique for understanding convergence of learning agents under small modifications of data. We show that such convergence can be understood via an analogue of Fatou's lemma which yields gamma-convergence. We show it's relevance and applications in general machine learning tasks and domain adaptation transfer learning.