Multi-Task Active Learning with Efficient Resource Allocation
Read the original on arXiv Statistics ML →The Flow has not summarised this story yet — read it at arXiv Statistics ML.
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arXiv:2608. 03511v1 Announce Type: cross Abstract: Active learning (AL) promises to reduce the cost of medical imaging projects by lowering the number of clinical labels required.
arXiv:2510. 12624v2 Announce Type: replace-cross Abstract: Active feature acquisition (AFA) is a sequential decision-making problem where the goal is to improve model performance for test instances by adaptively selecting which features to acquire.
arXiv:2610.23477v1 Announce Type: cross Abstract: We consider the problem of online linear classification, where a stream of unlabeled samples is available, and annotation is costly. We investigate t...
The paper critiques current multi‑task active learning (MTL‑AL) methods that use linear scalarization of objectives like uncertainty, diversity, and gradient conflict, arguing that such combinations miss Pareto‑optimal trade‑offs. It introduces a new framework that separates acquisition into informativeness and exploration, decouples conflict scoring from candidate ranking, and uses gradient surgery during training. The authors also propose a cross‑task mutual information metric and demonstrate consistent improvements on COCO, Cityscapes, and NYUv2 datasets compared to recent baselines.
arXiv:2405. 08921v2 Announce Type: replace Abstract: We focus on the online-based active learning (OAL) setting where an agent operates over a stream of observations and trades-off between the costly acquisition of information (labelled observations) and the cost of prediction errors.
arXiv:2512.12870v2 Announce Type: replace-cross Abstract: Active Learning (AL) is commonly used in applications where labeling data is expensive or time-consuming. In practice, however, labels are of...