# Measuring the Feasibility of Analogical Transfer using Complexity

arXiv: https://arxiv.org/abs/2206.11753 · HTML: https://mlsanity.com/paper/2206.11753/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 23 Jun 2022.

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

- [A Brief Review of Domain Adaptation](https://mlsanity.com/paper/2010.03978/) (2020)
- [Generalization Bounds for Domain Adaptation](https://mlsanity.com/paper/1304.1574/) (2013)
- [Analogical proportions](https://mlsanity.com/paper/2006.02854/) (2020)
- [A Neural Approach for Detecting Morphological Analogies](https://mlsanity.com/paper/2108.03945/) (2021)

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

- [SememeLM: A Sememe Knowledge Enhanced Method for Long-tail Relation Representation](https://mlsanity.com/paper/2406.10297/) (2024)
- [Proportional Analogies on Probability Distributions via Bayesian Updating](https://mlsanity.com/paper/2608.11724/) (2026)

## Abstract (arXiv)

Analogies are 4-ary relations of the form "A is to B as C is to D". While focus has been mostly on how to solve an analogy, i.e. how to find correct values of D given A, B and C, less attention has been drawn on whether solving such an analogy was actually feasible. In this paper, we propose a quantification of the transferability of a source case (A and B) to solve a target problem C. This quantification is based on a complexity minimization principle which has been demonstrated to be efficient for solving analogies. We illustrate these notions on morphological analogies and show its connections with machine learning, and in particular with Unsupervised Domain Adaptation.