Big Batch Bayesian Active Learning by Considering Predictive Probabilities

Sebastian W. Ober, Samuel Power, Tom Diethe, Henry B. Moss
1/14/2025
cs.LGstat.ML

No confident code implementation found for this paper.

Abstract

We observe that BatchBALD, a popular acquisition function for batch Bayesian active learning for classification, can conflate epistemic and aleatoric uncertainty, leading to suboptimal performance. Motivated by this observation, we propose to focus on the predictive probabilities, which only exhibit epistemic uncertainty. The result is an acquisition function that not only performs better, but is also faster to evaluate, allowing for larger batches than before.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Myopic Bayesian Decision Theory for Batch Active Learning with Partial Batch Label Sampling

88% match1 repo52025

Scalable Batch Acquisition for Deep Bayesian Active Learning

85% match1 repo42023

Prediction-Oriented Bayesian Active Learning

83% match1 repo412023

Improving Active Learning with a Bayesian Representation of Epistemic Uncertainty

83% match2024

Speeding Up BatchBALD: A k-BALD Family of Approximations for Active Learning

82% match2023

Understanding Uncertainty-based Active Learning Under Model Mismatch

82% match2024

Cite this paper

@article{ober2025batch,
  title  = {Big Batch Bayesian Active Learning by Considering Predictive Probabilities},
  author = {Sebastian W. Ober and Samuel Power and Tom Diethe and Henry B. Moss},
  year   = {2025},
  eprint = {2501.08223},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.08223v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2501.08223.svg)](https://codeofpaper.com/paper/2501.08223)

Discussion