# Toward Optimal Probabilistic Active Learning Using a Bayesian Approach

arXiv: https://arxiv.org/abs/2006.01732 · HTML: https://mlsanity.com/paper/2006.01732/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification](https://mlsanity.com/paper/2109.11301/) (2021)
- [Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving](https://mlsanity.com/paper/2210.08885/) (2022)
- [BoSS: A Best-of-Strategies Selector as an Oracle for Deep Active Learning](https://mlsanity.com/paper/2603.13109/) (2026)
- [Accumulative SGD Influence Estimation for Data Attribution](https://mlsanity.com/paper/2510.26185/) (2025)
- [Unraveling the Complexity of Splitting Sequential Data: Tackling Challenges in Video and Time Series Analysis](https://mlsanity.com/paper/2307.14294/) (2023)
- [Criteria for Uncertainty-based Corner Cases Detection in Instance Segmentation](https://mlsanity.com/paper/2404.11266/) (2024)

## Abstract (arXiv)

Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications. Active learning aims at reducing the labeling costs by an efficient and effective allocation of costly labeling resources. In this article, we propose a decision-theoretic selection strategy that (1) directly optimizes the gain in misclassification error, and (2) uses a Bayesian approach by introducing a conjugate prior distribution to determine the class posterior to deal with uncertainties. By reformulating existing selection strategies within our proposed model, we can explain which aspects are not covered in current state-of-the-art and why this leads to the superior performance of our approach. Extensive experiments on a large variety of datasets and different kernels validate our claims.