# Pool-Based Sequential Active Learning for Regression

arXiv: https://arxiv.org/abs/1805.04735 · HTML: https://mlsanity.com/paper/1805.04735/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2018.

## Built on by (most cited first, 30 of 33)

- [A Framework and Benchmark for Deep Batch Active Learning for Regression](https://mlsanity.com/paper/2203.09410/) (2022)
- [Bayesian Active Learning with Fully Bayesian Gaussian Processes](https://mlsanity.com/paper/2205.10186/) (2022)
- [Active Learning for Neural PDE Solvers](https://mlsanity.com/paper/2408.01536/) (2024)
- [Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal](https://mlsanity.com/paper/2303.01560/) (2023)
- [Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choices](https://mlsanity.com/paper/2403.03589/) (2024)
- [Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications](https://mlsanity.com/paper/2203.10794/) (2022)
- [Active Learning for Regression by Inverse Distance Weighting](https://mlsanity.com/paper/2204.07177/) (2022)
- [Image Quality Assessment: Integrating Model-Centric and Data-Centric Approaches](https://mlsanity.com/paper/2207.14769/) (2022)
- [Active Learning with Selective Time-Step Acquisition for PDEs](https://mlsanity.com/paper/2511.18107/) (2025)
- [Bucketized Active Sampling for Learning ACOPF](https://mlsanity.com/paper/2208.07497/) (2022)
- [Active Cost-aware Labeling of Streaming Data](https://mlsanity.com/paper/2304.06808/) (2023)
- [Progressive Generalization Risk Reduction for Data-Efficient Causal Effect Estimation](https://mlsanity.com/paper/2411.11256/) (2024)
- [Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective](https://mlsanity.com/paper/2505.05242/) (2025)
- [PyRelationAL: a python library for active learning research and development](https://mlsanity.com/paper/2205.11117/) (2022)
- [Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local Filter](https://mlsanity.com/paper/2306.09321/) (2023)
- [Streaming Active Learning for Regression Problems Using Regression via Classification](https://mlsanity.com/paper/2309.01013/) (2023)
- ["Forgetting" in Machine Learning and Beyond: A Survey](https://mlsanity.com/paper/2405.20620/) (2024)
- [Efficient Biological Data Acquisition through Inference Set Design](https://mlsanity.com/paper/2410.19631/) (2024)
- [Development and Application of Self-Supervised Machine Learning for Smoke Plume and Active Fire Identification from the FIREX-AQ Datasets](https://mlsanity.com/paper/2501.15343/) (2025)
- [Distribution Matching for Machine Teaching](https://mlsanity.com/paper/2105.13809/) (2021)
- [Does Deep Active Learning Work in the Wild?](https://mlsanity.com/paper/2302.00098/) (2023)
- [Human in the AI loop via xAI and Active Learning for Visual Inspection](https://mlsanity.com/paper/2307.05508/) (2023)
- [Sample Efficient Robot Learning in Supervised Effect Prediction Tasks](https://mlsanity.com/paper/2412.02331/) (2024)
- [Constraint-Informed Active Learning for End-to-End ACOPF Optimization Proxies](https://mlsanity.com/paper/2511.06248/) (2025)
- [MDS-VQA: Model-Informed Data Selection for Video Quality Assessment](https://mlsanity.com/paper/2603.11525/) (2026)
- [Adaptive Local Kernels Formulation of Mutual Information with Application to Active Post-Seismic Building Damage Inference](https://mlsanity.com/paper/2105.11492/) (2021)
- [Optimal Sampling Density for Nonparametric Regression](https://mlsanity.com/paper/2105.11990/) (2021)
- [From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report)](https://mlsanity.com/paper/2204.12302/) (2022)
- [Classification Tree-based Active Learning: A Wrapper Approach](https://mlsanity.com/paper/2404.09953/) (2024)
- [Cell Library Characterization for Composite Current Source Models Based on Gaussian Process Regression and Active Learning](https://mlsanity.com/paper/2505.10799/) (2025)

## Abstract (arXiv)

Active learning is a machine learning approach for reducing the data labeling effort. Given a pool of unlabeled samples, it tries to select the most useful ones to label so that a model built from them can achieve the best possible performance. This paper focuses on pool-based sequential active learning for regression (ALR). We first propose three essential criteria that an ALR approach should consider in selecting the most useful unlabeled samples: informativeness, representativeness, and diversity, and compare four existing ALR approaches against them. We then propose a new ALR approach using passive sampling, which considers both the representativeness and the diversity in both the initialization and subsequent iterations. Remarkably, this approach can also be integrated with other existing ALR approaches in the literature to further improve the performance. Extensive experiments on 11 UCI, CMU StatLib, and UFL Media Core datasets from various domains verified the effectiveness of our proposed ALR approaches.