# kNNSampler: Stochastic Imputations for Recovering Missing Value Distributions

arXiv: https://arxiv.org/abs/2509.08366 · HTML: https://mlsanity.com/paper/2509.08366/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 47 references that are in the map.
- First posted on arXiv 10 Sep 2025.

## Builds on (most cited first, 11 of 11)

- [Kernel Mean Embedding of Distributions: A Review and Beyond](https://mlsanity.com/paper/1605.09522/) (2016)
- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares](https://mlsanity.com/paper/1410.2596/) (2014)
- [Optimal Rates for Regularized Conditional Mean Embedding Learning](https://mlsanity.com/paper/2208.01711/) (2022)
- [Metrizing Weak Convergence with Maximum Mean Discrepancies](https://mlsanity.com/paper/2006.09268/) (2020)
- [Random Forest Missing Data Algorithms](https://mlsanity.com/paper/1701.05305/) (2017)
- [Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm](https://mlsanity.com/paper/2312.07186/) (2023)
- [Multiple Imputation: A Review of Practical and Theoretical Findings](https://mlsanity.com/paper/1801.04058/) (2018)
- [Fast Computation of Leave-One-Out Cross-Validation for k-NN Regression](https://mlsanity.com/paper/2405.04919/) (2024)
- [Numerical Data Imputation for Multimodal Data Sets: A Probabilistic Nearest-Neighbor Kernel Density Approach](https://mlsanity.com/paper/2306.16906/) (2023)
- [Imputation Scores](https://mlsanity.com/paper/2106.03742/) (2021)

## Built on by (most cited first, 1 of 1)

- [Predictive Uncertainty in Short-Term PV Forecasting under Missing Data: A Multiple Imputation Approach](https://mlsanity.com/paper/2603.15564/) (2026)

## Abstract (arXiv)

We study a missing-value imputation method, termed kNNSampler, that imputes a given unit's missing response by randomly sampling from the observed responses of the k most similar units to the given unit in terms of the observed covariates. This method can sample unknown missing values from their distributions, quantify the uncertainties of missing values, and be readily used for multiple imputation. Unlike popular kNNImputer, which estimates the conditional mean of a missing response given an observed covariate, kNNSampler is theoretically shown to estimate the conditional distribution of a missing response given an observed covariate. Experiments illustrate the performance of kNNSampler. The code for kNNSampler is made publicly available (https://github.com/SAP/knn-sampler).