arXiv:2610.07045v1 Announce Type: cross Abstract: Many scientific studies allow costly auxiliary information to be collected during data labeling but not at deployment. Examples include diagnostic te...
arXiv:2307.02719v5 Announce Type: replace Abstract: Uncertainty sampling is a classical active-learning strategy, yet the statistical objective induced by its query rule is often implicit. We introdu...
arXiv:2608. 03249v1 Announce Type: new Abstract: Cold-Start Active Learning (CSAL) aims to select a valuable subset from an unlabeled pool without any prior knowledge or human assistance.
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:2610. 10499v1 Announce Type: new Abstract: Online learning is an attractive framework in many domains because it permits well-defined learning even when data are dependent or chosen adversarially.
arXiv:2607. 10694v1 Announce Type: cross Abstract: We study the problem of optimal continual fine-tuning for a pre-trained Foundation Model deployed at a resource-limited device.
The paper introduces a low-budget active learning approach that selects a small coreset of data points for training high-accuracy models, particularly useful when labeling is expensive, such as in medical contexts. It uses features from a pretrained self-supervised model and applies entropic optimal transport—specifically the Sinkhorn divergence—as the selection criterion, enabling dimension-free sample complexity and efficient gradient-based optimization. The method combines gradient-based candidate generation with a swap-based local search, achieving superior performance over existing heuristics on image and medical datasets.
arXiv:2004. 06321v2 Announce Type: replace Abstract: We study the sequential batch learning problem in linear contextual bandits with finite action sets, where the decision maker is constrained to split incoming individuals into (at most) a fixed number of batches and can only observe outcomes for the individuals within a batch at the batch's end.
arXiv:2606. 17805v1 Announce Type: new Abstract: Data acquisition is a major bottleneck for learning in real-time streams: analysts must decide on the fly which labels to purchase while respecting a rolling budget.
arXiv:2606. 11130v1 Announce Type: new Abstract: We study the task of agnostically learning general (as opposed to homogeneous) ReLUs under the Gaussian distribution with respect to the squared loss.
arXiv:2606. 03831v1 Announce Type: new Abstract: This paper investigates non-stationary online learning using the metric of interval regret, which requires an online algorithm to perform well over every time interval.
The paper investigates the difference between cost‑agnostic and cost‑sensitive loss functions when model capacity is limited. It shows that, unlike in ideal infinite‑capacity settings, optimizing a cost‑sensitive objective can yield a strictly better downstream decision than post‑processing a cost‑agnostic model. The authors prove this gap under a hypothesis class that can recover the optimal decision boundary but not the optimal cost‑agnostic hypothesis, and provide a simple example and empirical evidence on UCI datasets with simple models.