Related papers: Prediction-Oriented Bayesian Active Learning

Prediction-Oriented Bayesian Active Learning

URL: http://arxiv.org/abs/2304.08151v1
Date: Mon, 17 Apr 2023 10:59:57 GMT
Title: Prediction-Oriented Bayesian Active Learning
Authors: Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth
Abstract summary: Expected predictive information gain (EPIG) is an acquisition function that measures information gain in the space of predictions rather than parameters. EPIG leads to stronger predictive performance compared with BALD across a range of datasets and models.
Score: 51.426960808684655
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Information-theoretic approaches to active learning have traditionally focused on maximising the information gathered about the model parameters, most commonly by optimising the BALD score. We highlight that this can be suboptimal from the perspective of predictive performance. For example, BALD lacks a notion of an input distribution and so is prone to prioritise data of limited relevance. To address this we propose the expected predictive information gain (EPIG), an acquisition function that measures information gain in the space of predictions rather than parameters. We find that using EPIG leads to stronger predictive performance compared with BALD across a range of datasets and models, and thus provides an appealing drop-in replacement.

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