# Active Learning for Regression by Inverse Distance Weighting

arXiv: https://arxiv.org/abs/2204.07177 · HTML: https://mlsanity.com/paper/2204.07177/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 14 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [Efficient representation and approximation of model predictive control laws via deep learning](https://mlsanity.com/paper/1806.10644/) (2018)
- [Pool-Based Sequential Active Learning for Regression](https://mlsanity.com/paper/1805.04735/) (2018)
- [Active Learning for Regression Using Greedy Sampling](https://mlsanity.com/paper/1808.04245/) (2018)
- [Global optimization via inverse distance weighting and radial basis functions](https://mlsanity.com/paper/1906.06498/) (2019)
- [Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)](https://mlsanity.com/paper/2003.07658/) (2020)
- [Active preference learning based on radial basis functions](https://mlsanity.com/paper/1909.13049/) (2019)

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

- [Distributionally robust minimization in meta-learning for system identification](https://mlsanity.com/paper/2506.18074/) (2025)
- [Streaming Active Learning for Regression Problems Using Regression via Classification](https://mlsanity.com/paper/2309.01013/) (2023)
- [An active learning method for solving competitive multi-agent decision-making and control problems](https://mlsanity.com/paper/2212.12561/) (2022)
- [Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks](https://mlsanity.com/paper/2403.15108/) (2024)
- [Nonmyopic Global Optimisation via Approximate Dynamic Programming](https://mlsanity.com/paper/2412.04882/) (2024)
- [Budget-constrained Active Learning to Effectively De-censor Survival Data](https://mlsanity.com/paper/2510.12144/) (2025)
- [Adaptive Active Learning for Regression via Reinforcement Learning](https://mlsanity.com/paper/2603.10435/) (2026)
- [TCL: Enabling Fast and Efficient Cross-Hardware Tensor Program Optimization via Continual Learning](https://mlsanity.com/paper/2604.12891/) (2026)
- [Harnessing Source Heterogeneity for Cluster-Structured Transfer Learning](https://mlsanity.com/paper/2606.05258/) (2026)

## Abstract (arXiv)

This paper proposes an active learning (AL) algorithm to solve regression problems based on inverse-distance weighting functions for selecting the feature vectors to query. The algorithm has the following features: (i) supports both pool-based and population-based sampling; (ii) is not tailored to a particular class of predictors; (iii) can handle known and unknown constraints on the queryable feature vectors; and (iv) can run either sequentially, or in batch mode, depending on how often the predictor is retrained. The potentials of the method are shown in numerical tests on illustrative synthetic problems and real-world datasets. An implementation of the algorithm, which we call IDEAL (Inverse-Distance based Exploration for Active Learning), is available at http://cse.lab.imtlucca.it/~bemporad/ideal.