mlsanityData to 7 Oct 2026

Active Learning for Regression by Inverse Distance Weighting

arXiv 2204.07177 ↗First posted 14 Apr 2022cs.LGcited by 9

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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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 6 of its 44 references that are in the map. First posted on arXiv 14 Apr 2022.

Builds on

6 of 44 references.

References · 6

  1. 2018Efficient representation and approximation of model predictive control laws via deep learningcited by 55
  2. 2018Pool-Based Sequential Active Learning for Regressioncited by 33
  3. 2018Active Learning for Regression Using Greedy Samplingcited by 26
  4. 2019Global optimization via inverse distance weighting and radial basis functionscited by 16
  5. 2020Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)cited by 7
  6. 2019Active preference learning based on radial basis functionscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 2; 2026: 3.

9 papers.

  1. 2023Streaming Active Learning for Regression Problems Using Regression via Classificationcited by 2
  2. 2025Distributionally robust minimization in meta-learning for system identificationcited by 2
  3. 2022An active learning method for solving competitive multi-agent decision-making and control problemscited by 0
  4. 2024Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networkscited by 0
  5. 2024Nonmyopic Global Optimisation via Approximate Dynamic Programmingcited by 0
  6. 2025Budget-constrained Active Learning to Effectively De-censor Survival Datacited by 0
  7. 2026Adaptive Active Learning for Regression via Reinforcement Learningcited by 0
  8. 2026TCL: Enabling Fast and Efficient Cross-Hardware Tensor Program Optimization via Continual Learningcited by 0
  9. 2026Harnessing Source Heterogeneity for Cluster-Structured Transfer Learningcited by 0