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...
arXiv:2605. 31014v2 Announce Type: replace Abstract: Multi-omics data provide complementary molecular characterizations of disease phenotypes and play an important role in disease diagnosis and subtype classification in precision medicine.
arXiv:2604. 17267v2 Announce Type: replace Abstract: Large Language Models can generate synthetic survey responses at low cost, but their accuracy varies unpredictably across questions.
The paper introduces Temporal Difference Active Learning (TDAL), a method that learns an acquisition function online for perturbation discovery by treating the problem as a sequential decision task. TDAL uses temporal difference learning with the Hellinger distance to weight future value and employs leave‑batch‑out to create virtual transitions, thereby addressing data scarcity. Evaluated on eight public screens involving single, double, and triple perturbations, TDAL outperforms eleven baseline methods, achieving an average rank of 1.63 and topping four tasks.
arXiv:2605. 23268v2 Announce Type: replace-cross Abstract: In many prediction problems, we have extra information during training (for example, measurements that are expensive or slow to collect) that will not be available when the model is deployed.
arXiv:2606. 07141v1 Announce Type: cross Abstract: Language models trained for clinical disease inference are trained on patient data, which may include sensitive and private information, and data owners may request the removal of their data from a trained model due to privacy or copyright concerns.
arXiv:2607. 00144v1 Announce Type: cross Abstract: Active learning (AL) performance is known to be budget-dependent, yet regimes are typically defined by heuristic label counts that fail to generalize across datasets or architectures.