# Active Learning for Regression Using Greedy Sampling

arXiv: https://arxiv.org/abs/1808.04245 · HTML: https://mlsanity.com/paper/1808.04245/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2018.

## Built on by (most cited first, 26 of 26)

- [Bayesian Active Learning with Fully Bayesian Gaussian Processes](https://mlsanity.com/paper/2205.10186/) (2022)
- [Troubleshooting Blind Image Quality Models in the Wild](https://mlsanity.com/paper/2105.06747/) (2021)
- [Active Learning for Regression by Inverse Distance Weighting](https://mlsanity.com/paper/2204.07177/) (2022)
- [Backdoor Attack and Defense for Deep Regression](https://mlsanity.com/paper/2109.02381/) (2021)
- [An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasets](https://mlsanity.com/paper/2306.08954/) (2023)
- [Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition](https://mlsanity.com/paper/2412.01171/) (2024)
- [Dynamic Exploration-Exploitation Trade-Off in Active Learning Regression with Bayesian Hierarchical Modeling](https://mlsanity.com/paper/2304.07665/) (2023)
- [Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local Filter](https://mlsanity.com/paper/2306.09321/) (2023)
- [On minimizing the training set fill distance in machine learning regression](https://mlsanity.com/paper/2307.10988/) (2023)
- [Streaming Active Learning for Regression Problems Using Regression via Classification](https://mlsanity.com/paper/2309.01013/) (2023)
- [Robust and Active Learning for Deep Neural Network Regression](https://mlsanity.com/paper/2107.13124/) (2021)
- [An Interactive Visualization Tool for Understanding Active Learning](https://mlsanity.com/paper/2111.04936/) (2021)
- [Does Deep Active Learning Work in the Wild?](https://mlsanity.com/paper/2302.00098/) (2023)
- [Active learning for regression in engineering populations: A risk-informed approach](https://mlsanity.com/paper/2409.04328/) (2024)
- [Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes](https://mlsanity.com/paper/2410.02145/) (2024)
- [Sample Efficient Robot Learning in Supervised Effect Prediction Tasks](https://mlsanity.com/paper/2412.02331/) (2024)
- [Unsupervised Instance Selection with Low-Label, Supervised Learning for Outlier Detection](https://mlsanity.com/paper/2104.12837/) (2021)
- [Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-Supervised Learning](https://mlsanity.com/paper/2110.03765/) (2021)
- [Through-life Monitoring of Resource-constrained Systems and Fleets](https://mlsanity.com/paper/2301.01017/) (2023)
- [Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement Learning](https://mlsanity.com/paper/2301.13343/) (2023)
- [Model Uncertainty based Active Learning on Tabular Data using Boosted Trees](https://mlsanity.com/paper/2310.19573/) (2023)
- [Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks](https://mlsanity.com/paper/2403.15108/) (2024)
- [Classification Tree-based Active Learning: A Wrapper Approach](https://mlsanity.com/paper/2404.09953/) (2024)
- [Regression generation adversarial network based on dual data evaluation strategy for industrial application](https://mlsanity.com/paper/2512.19232/) (2025)
- [Accelerated Discovery of Cryoprotectant Cocktails via Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2602.13398/) (2026)
- [Feature Weighting Improves Pool-Based Sequential Active Learning for Regression](https://mlsanity.com/paper/2604.02019/) (2026)

## Abstract (arXiv)

Regression problems are pervasive in real-world applications. Generally a substantial amount of labeled samples are needed to build a regression model with good generalization ability. However, many times it is relatively easy to collect a large number of unlabeled samples, but time-consuming or expensive to label them. Active learning for regression (ALR) is a methodology to reduce the number of labeled samples, by selecting the most beneficial ones to label, instead of random selection. This paper proposes two new ALR approaches based on greedy sampling (GS). The first approach (GSy) selects new samples to increase the diversity in the output space, and the second (iGS) selects new samples to increase the diversity in both input and output spaces. Extensive experiments on 12 UCI and CMU StatLib datasets from various domains, and on 15 subjects on EEG-based driver drowsiness estimation, verified their effectiveness and robustness.