CAAL: Contextual Bandits based Online Hand-Craft Active Learning Strategy Selection
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Abstract (arXiv)
The challenge with active learning algorithms is the uncertainty of the statistical distribution of unlabeled data, making it difficult to choose the best hand-crafted strategy. To address this, we introduced Contextual Adaptive Active Learning (CAAL). In CAAL, each "arm" represents a hand-crafted strategy. Unlike existing frameworks that select strategies based only on feedback from labeled data, we dynamically choose strategies for labeling batches of data using reward prediction with external context information. This general framework allows for customization with domain knowledge to design more effective rewards and context candidates. In addition, we experimentally show that CAAL outperforms the existing baseline adaptive strategy on public datasets using our reward and context design. Our results are consistent regardless of batch size in each iteration.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 24 references that are in the map. First posted on arXiv 6 Jun 2026.
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References · 11
- 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 602
- 2021Tabular Data: Deep Learning is Not All You Needcited by 530
- 2019BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learningcited by 428
- 2021Batch Active Learning at Scalecited by 127
- 2017Learning Active Learning from Datacited by 100
- 2021Gone Fishing: Neural Active Learning with Fisher Embeddingscited by 88
- 2018Learning to Optimize under Non-Stationaritycited by 85
- 2019Weighted Linear Bandits for Non-Stationary Environmentscited by 84
- 2019Active Adversarial Domain Adaptationcited by 73
- 2020Reinforced active learning for image segmentationcited by 56
- 2023Algorithm Selection for Deep Active Learning with Imbalanced Datasetscited by 18
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