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Diversity Enhanced Active Learning with Strictly Proper Scoring Rules
Wei Tan · Lan Du · Wray Buntine

Wed Dec 08 04:30 PM -- 06:00 PM (PST) @ Virtual

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.

Author Information

Wei Tan (Monash University)
Lan Du (Monash University)
Wray Buntine (Monash University)

Wray Buntine is a full professor at Monash University where he is directory of the Machine Learning Group. He was previously at NICTA in Canberra, Helsinki Institute for Information Technology where he ran a semantic search project, NASA Ames Research Center, University of California, Berkeley, and Google. In the '90s he was involved in a string of startups for both Wall Street and Silicon Valley. He is known for Bayesian machine learning, non-parametrics and document analysis, having been a driving force in the use of ensembling, graphical models, and nonparametric algorithms.

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