# Approximately optimal domain adaptation with Fisher's Linear Discriminant

arXiv: https://arxiv.org/abs/2302.14186 · HTML: https://mlsanity.com/paper/2302.14186/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 66 references that are in the map.
- First posted on arXiv 27 Feb 2023.

## Builds on (most cited first, 14 of 14)

- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Reconciling modern machine learning practice and the bias-variance trade-off](https://mlsanity.com/paper/1812.11118/) (2018)
- [Taskonomy: Disentangling Task Transfer Learning](https://mlsanity.com/paper/1804.08328/) (2018)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [LEEP: A New Measure to Evaluate Transferability of Learned Representations](https://mlsanity.com/paper/2002.12462/) (2020)
- [Online Meta-Learning](https://mlsanity.com/paper/1902.08438/) (2019)
- [Transferability and Hardness of Supervised Classification Tasks](https://mlsanity.com/paper/1908.08142/) (2019)
- [Multi-Source Domain Adaptation with Mixture of Experts](https://mlsanity.com/paper/1809.02256/) (2018)
- [The Value of Out-of-Distribution Data](https://mlsanity.com/paper/2208.10967/) (2022)
- [The Effect of Person-Specific Biometrics in Improving Generic Stress Predictive Models](https://mlsanity.com/paper/1910.01770/) (2019)
- [Simple Lifelong Learning Machines](https://mlsanity.com/paper/2004.12908/) (2020)
- [Mental State Classification Using Multi-graph Features](https://mlsanity.com/paper/2203.00516/) (2022)
- [A partition-based similarity for classification distributions](https://mlsanity.com/paper/2011.06557/) (2020)
- [Leveraging semantically similar queries for ranking via combining representations](https://mlsanity.com/paper/2106.12621/) (2021)

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

- [Query-efficient model evaluation using cached responses](https://mlsanity.com/paper/2605.07096/) (2026)
- [Transfer learning via Regularized Linear Discriminant Analysis](https://mlsanity.com/paper/2501.02411/) (2025)

## Abstract (arXiv)

We propose a class of models based on Fisher's Linear Discriminant (FLD) in the context of domain adaptation. The class is the convex combination of two hypotheses: i) an average hypothesis representing previously seen source tasks and ii) a hypothesis trained on a new target task. For a particular generative setting we derive the optimal convex combination of the two models under 0-1 loss, propose a computable approximation, and study the effect of various parameter settings on the relative risks between the optimal hypothesis, hypothesis i), and hypothesis ii). We demonstrate the effectiveness of the proposed optimal classifier in the context of EEG- and ECG-based classification settings and argue that the optimal classifier can be computed without access to direct information from any of the individual source tasks. We conclude by discussing further applications, limitations, and possible future directions.