aiqbench analysis
This paper addresses the computational inefficiency of batch Bayesian active learning by introducing Partial Batch Label Sampling (ParBaLS) to improve performance across various datasets and budgets. By extending the Bayesian Decision Theory framework specifically for the EPIG algorithm, it offers a practical solution to the trade-offs between accuracy and resource constraints in labeling strategies. The approach leverages embeddings from large pre-trained models, demonstrating its utility in optimizing data selection workflows for machine learning practitioners.
Batch Bayesian Active Learning with Partial Batch Label Sampling
arXiv:2510.09877v4 Announce Type: replace-cross Abstract: Over the past couple of decades, many active learning acquisition functions have been proposed, leaving practitioners with an unclear choice of which to use. Bayesian active learning offers principled objectives with explainable intuition, including Expected Error Reduction (EER), Expected Predictive Information Gain (EPIG), and Bayesian Active Learning by Disagreements (BALD). A key challenge of such methods is the difficulty of extending to the batch setting, leading to either computational challenges (BatchBALD) or dramatic performance drops (top-$B$ selection). In this work, we extend active learning's Bayesian Decision Theory framework to derive Partial Batch Label Sampling (ParBaLS) for the EPIG algorithm. We show experimentally that ParBaLS EPIG gives superior performance for a variety of datasets with different budgets using Bayesian logistic regression on embeddings from large pre-trained models. Our code is available at https://github.com/ADDAPT-ML/ParBaLS.
Abstract:Over the past couple of decades, many active learning acquisition functions have been proposed, leaving practitioners with an unclear choice of which to use. Bayesian active learning offers principled objectives with explainable intuition, including Expected Error Reduction (EER), Expected Predictive Information Gain (EPIG), and Bayesian Active Learning by Disagreements (BALD). A key challenge of such methods is the difficulty of extending to the batch setting, leading to either computational challenges (BatchBALD) or dramatic performance drops (top-$B$ selection). In this work, we extend active learning's Bayesian Decision Theory framework to derive Partial Batch Label Sampling (ParBaLS) for the EPIG algorithm. We show experimentally that ParBaLS EPIG gives superior performance for a variety of datasets with different budgets using Bayesian logistic regression on embeddings from large pre-trained models. Our code is available at this https URL.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
arXiv:2510.09877 [cs.LG]
(or arXiv:2510.09877v4 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2510.09877 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 232 words
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