# Some recent advances in reasoning based on analogical proportions

arXiv: https://arxiv.org/abs/2212.11717 · HTML: https://mlsanity.com/paper/2212.11717/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 22 Dec 2022.

## Builds on (most cited first, 4 of 4)

- [Explanations for Monotonic Classifiers](https://mlsanity.com/paper/2106.00154/) (2021)
- [Analogical Dissimilarity: Definition, Algorithms and Two Experiments in Machine Learning](https://mlsanity.com/paper/1401.3427/) (2014)
- [Analogical proportions](https://mlsanity.com/paper/2006.02854/) (2020)
- [Towards Analogy-Based Explanations in Machine Learning](https://mlsanity.com/paper/2005.12800/) (2020)

## Abstract (arXiv)

Analogical proportions compare pairs of items (a, b) and (c, d) in terms of their differences and similarities. They play a key role in the formalization of analogical inference. The paper first discusses how to improve analogical inference in terms of accuracy and in terms of computational cost. Then it indicates the potential of analogical proportions for explanation. Finally, it highlights the close relationship between analogical proportions and multi-valued dependencies, which reveals an unsuspected aspect of the former.