# Nearest Neighbor-based Importance Weighting

arXiv: https://arxiv.org/abs/2102.02291 · HTML: https://mlsanity.com/paper/2102.02291/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 22 references that are in the map.
- First posted on arXiv 3 Feb 2021.

## Built on by (most cited first, 9 of 9)

- [Featurized Density Ratio Estimation](https://mlsanity.com/paper/2107.02212/) (2021)
- [ADAPT : Awesome Domain Adaptation Python Toolbox](https://mlsanity.com/paper/2107.03049/) (2021)
- [Fast and Accurate Importance Weighting for Correcting Sample Bias](https://mlsanity.com/paper/2209.04215/) (2022)
- [SPINEX: Similarity-based Predictions and Explainable Neighbors Exploration for Regression and Classification Tasks in Machine Learning](https://mlsanity.com/paper/2306.01029/) (2023)
- [SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities](https://mlsanity.com/paper/2407.11676/) (2024)
- [Stratified Learning: A General-Purpose Statistical Method for Improved Learning under Covariate Shift](https://mlsanity.com/paper/2106.11211/) (2021)
- [Spatial Transfer Learning for Estimating PM2.5 in Data-poor Regions](https://mlsanity.com/paper/2404.07308/) (2024)
- [Domain Adaptation-Enhanced Searchlight: Enabling classification of brain states from visual perception to mental imagery](https://mlsanity.com/paper/2408.01163/) (2024)
- [ADAPTOOD: Uncertainty-Aware Fine-Tuning for Out-of-Distribution ECG Time Series Models](https://mlsanity.com/paper/2606.04164/) (2026)

## Abstract (arXiv)

Importance weighting is widely applicable in machine learning in general and in techniques dealing with data covariate shift problems in particular. A novel, direct approach to determine such importance weighting is presented. It relies on a nearest neighbor classification scheme and is relatively straightforward to implement. Comparative experiments on various classification tasks demonstrate the effectiveness of our so-called nearest neighbor weighting (NNeW) scheme. Considering its performance, our procedure can act as a simple and effective baseline method for importance weighting.