The effectiveness of active learning largely depends on the sampling
efficiency of the acquisition function. Expected Loss Reduction (ELR) focuses
on a Bayesian estimate of the reduction in classification error, and more
general costs fit in the same framework. We propose Bayesian Estimate of Mean
Proper Scores (BEMPS) to estimate the increase in strictly proper scores such
as log probability or negative mean square error within this framework. We also
prove convergence results for this general class of costs. To facilitate better
experimentation with the new acquisition functions, we develop a complementary
batch AL algorithm that encourages diversity in the vector of expected changes
in scores for unlabeled data. To allow high-performance classifiers, we combine
deep ensembles, and dynamic validation set construction on pretrained models,
and further speed up the ensemble process with the idea of Monte Carlo Dropout.
Extensive experiments on both texts and images show that the use of mean square
error and log probability with BEMPS yields robust acquisition functions and
well-calibrated classifiers, and consistently outperforms the others tested.
The advantages of BEMPS over the others are further supported by a set of
qualitative analyses, where we visualise their sampling behaviour using data
maps and t-SNE plots.

提出了一种基于贝叶斯估计的增加严格适当得分的方法 BEMPS，用于在主动学习中估计严格适当得分的增加，该方法在实验证明了其较其他方法更强大和更具鲁棒性，并通过定性分析得到了支持。

多样性增强主动学习的均值适当得分的贝叶斯估计

Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active  Learning

We study acquisition functions for active learning (AL) for text
classification. The Expected Loss Reduction (ELR) method focuses on a Bayesian
estimate of the reduction in classification error, recently updated with Mean
Objective Cost of Uncertainty (MOCU). We convert the ELR framework to estimate
the increase in (strictly proper) scores like log probability or negative mean
square error, which we call Bayesian Estimate of Mean Proper Scores (BEMPS). We
also prove convergence results borrowing techniques used with MOCU. In order to
allow better experimentation with the new acquisition functions, we develop a
complementary batch AL algorithm, which encourages diversity in the vector of
expected changes in scores for unlabelled data. To allow high performance text
classifiers, we combine ensembling and dynamic validation set construction on
pretrained language models. Extensive experimental evaluation then explores how
these different acquisition functions perform. The results show that the use of
mean square error and log probability with BEMPS yields robust acquisition
functions, which consistently outperform the others tested.

本研究探讨文本分类的主动学习领域中的不同收集函数，并开发了一种用于未标记数据的预期分数变化向量的多样性的批量主动学习算法，最后结合集成式学习和动态验证集构建证明 BEMPS 的 mean square error 和 log probability 收集函数的优越表现。