# Twin Neural Network Improved k-Nearest Neighbor Regression

arXiv: https://arxiv.org/abs/2310.00664 · HTML: https://mlsanity.com/paper/2310.00664/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 32 references that are in the map.
- First posted on arXiv 1 Oct 2023.

## Builds on (most cited first, 2 of 2)

- [Twin Neural Network Regression](https://mlsanity.com/paper/2012.14873/) (2020)
- [Twin Neural Network Regression is a Semi-Supervised Regression Algorithm](https://mlsanity.com/paper/2106.06124/) (2021)

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

- [Inverting Non-Injective Functions with Twin Neural Network Regression](https://mlsanity.com/paper/2601.05378/) (2026)
- [Fitting scattered data with optional monotonicity constraints on GPU: LipFit package](https://mlsanity.com/paper/2606.04670/) (2026)

## Abstract (arXiv)

Twin neural network regression is trained to predict differences between regression targets rather than the targets themselves. A solution to the original regression problem can be obtained by ensembling predicted differences between the targets of an unknown data point and multiple known anchor data points. Choosing the anchors to be the nearest neighbors of the unknown data point leads to a neural network-based improvement of k-nearest neighbor regression. This algorithm is shown to outperform both neural networks and k-nearest neighbor regression on small to medium-sized data sets.