Black-Box Batch Active Learning for Regression
- URL: http://arxiv.org/abs/2302.08981v2
- Date: Fri, 7 Jul 2023 10:49:10 GMT
- Title: Black-Box Batch Active Learning for Regression
- Authors: Andreas Kirsch
- Abstract summary: Batch active learning is a popular approach for efficiently training machine learning models on unlabelled datasets.
We propose black-box batch active learning for regression tasks as an extension of white-box approaches.
- Score: 1.52292571922932
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Batch active learning is a popular approach for efficiently training machine
learning models on large, initially unlabelled datasets by repeatedly acquiring
labels for batches of data points. However, many recent batch active learning
methods are white-box approaches and are often limited to differentiable
parametric models: they score unlabeled points using acquisition functions
based on model embeddings or first- and second-order derivatives. In this
paper, we propose black-box batch active learning for regression tasks as an
extension of white-box approaches. Crucially, our method only relies on model
predictions. This approach is compatible with a wide range of machine learning
models, including regular and Bayesian deep learning models and
non-differentiable models such as random forests. It is rooted in Bayesian
principles and utilizes recent kernel-based approaches. This allows us to
extend a wide range of existing state-of-the-art white-box batch active
learning methods (BADGE, BAIT, LCMD) to black-box models. We demonstrate the
effectiveness of our approach through extensive experimental evaluations on
regression datasets, achieving surprisingly strong performance compared to
white-box approaches for deep learning models.
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