# Active Learning Using Uncertainty Information

arXiv: https://arxiv.org/abs/1702.08540 · HTML: https://mlsanity.com/paper/1702.08540/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2017.

## Built on by (most cited first, 28 of 28)

- [Deep Active Learning by Leveraging Training Dynamics](https://mlsanity.com/paper/2110.08611/) (2021)
- [Diversity Enhanced Active Learning with Strictly Proper Scoring Rules](https://mlsanity.com/paper/2110.14171/) (2021)
- [Convergence of Uncertainty Sampling for Active Learning](https://mlsanity.com/paper/2110.15784/) (2021)
- [Systemizing Multiplicity: The Curious Case of Arbitrariness in Machine Learning](https://mlsanity.com/paper/2501.14959/) (2025)
- [Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning](https://mlsanity.com/paper/2202.12588/) (2022)
- [On the Intrinsic Self-Correction Capability of LLMs: Uncertainty and Latent Concept](https://mlsanity.com/paper/2406.02378/) (2024)
- [A Short Survey on Importance Weighting for Machine Learning](https://mlsanity.com/paper/2403.10175/) (2024)
- [Model Learning with Personalized Interpretability Estimation (ML-PIE)](https://mlsanity.com/paper/2104.06060/) (2021)
- [Does Informativeness Matter? Active Learning for Educational Dialogue Act Classification](https://mlsanity.com/paper/2304.05578/) (2023)
- [ProbVLM: Probabilistic Adapter for Frozen Vision-Language Models](https://mlsanity.com/paper/2307.00398/) (2023)
- [Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning](https://mlsanity.com/paper/2312.10116/) (2023)
- [B-BACN: Bayesian Boundary-Aware Convolutional Network for Crack Characterization](https://mlsanity.com/paper/2302.06827/) (2023)
- [Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications](https://mlsanity.com/paper/2209.06855/) (2022)
- [Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images](https://mlsanity.com/paper/2303.05225/) (2023)
- [Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKA](https://mlsanity.com/paper/2411.00259/) (2024)
- [Federated Learning-Enabled Hybrid Language Models for Communication-Efficient Token Transmission](https://mlsanity.com/paper/2507.00082/) (2025)
- [Measuring the Confidence of Traffic Forecasting Models: Techniques, Experimental Comparison and Guidelines towards Their Actionability](https://mlsanity.com/paper/2210.16049/) (2022)
- [MAViC: Multimodal Active Learning for Video Captioning](https://mlsanity.com/paper/2212.11109/) (2022)
- [On the Convergence of Loss and Uncertainty-based Active Learning Algorithms](https://mlsanity.com/paper/2312.13927/) (2023)
- [Sample Efficient Robot Learning in Supervised Effect Prediction Tasks](https://mlsanity.com/paper/2412.02331/) (2024)
- [Revisiting Unknowns: Towards Effective and Efficient Open-Set Active Learning](https://mlsanity.com/paper/2603.07898/) (2026)
- [Calibrating conditional risk](https://mlsanity.com/paper/2604.20409/) (2026)
- [Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity](https://mlsanity.com/paper/2110.11778/) (2021)
- [PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification](https://mlsanity.com/paper/2306.16918/) (2023)
- [Improve Cost Efficiency of Active Learning over Noisy Dataset](https://mlsanity.com/paper/2403.01346/) (2024)
- [Taylor-Sensus Network: Embracing Noise to Enlighten Uncertainty for Scientific Data](https://mlsanity.com/paper/2409.07942/) (2024)
- [Learning Coupled Subspaces for Multi-Condition Spike Data](https://mlsanity.com/paper/2410.19153/) (2024)
- [When Active Learning Falls Short: An Empirical Study on Chemical Reaction Extraction](https://mlsanity.com/paper/2604.19335/) (2026)

## Abstract (arXiv)

Many active learning methods belong to the retraining-based approaches, which select one unlabeled instance, add it to the training set with its possible labels, retrain the classification model, and evaluate the criteria that we base our selection on. However, since the true label of the selected instance is unknown, these methods resort to calculating the average-case or worse-case performance with respect to the unknown label. In this paper, we propose a different method to solve this problem. In particular, our method aims to make use of the uncertainty information to enhance the performance of retraining-based models. We apply our method to two state-of-the-art algorithms and carry out extensive experiments on a wide variety of real-world datasets. The results clearly demonstrate the effectiveness of the proposed method and indicate it can reduce human labeling efforts in many real-life applications.