arXiv:2511. 19359v2 Announce Type: replace Abstract: Conformal Prediction (CP) has emerged as a powerful statistical framework for high-stakes classification applications.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2606. 28598v1 Announce Type: cross Abstract: Prediction sets should have high coverage to be useful, but some coverage notions are more practically relevant than others.
Conformal prediction replaces single-class predictions with prediction sets that guarantee a pre-specified coverage probability. The paper reviews properties of non‑conformity score functions, presents examples from the literature, and proposes new modifications. It introduces a method to evaluate prediction set sizes and compares different score functions, including their effectiveness for class‑conditional conformal prediction with imbalanced classes.
The paper introduces a method for combining heterogeneous, allied datasets—datasets that share the same class labels but have disjoint objects and largely distinct feature spaces—into a single unified feature space. By applying matrix completion to this merged space, the authors create a unified dataset that enables knowledge transfer between the original datasets. Experiments across multiple dataset pairs and classifiers show that models trained on the unified representation consistently outperform those trained separately on each dataset.
CoLa-ICD is a knowledge‑enhanced framework designed to improve automatic medical coding of ICD codes in long, imbalanced clinical documents. It enriches ICD labels with external terms, models dependencies among related codes, and strengthens the alignment between label semantics and clinical evidence, particularly for rare codes. Experiments demonstrate that CoLa-ICD achieves state‑of‑the‑art performance in AUC, F1, and P@k, with larger gains in larger and sparser label spaces.
Automatic medical coding assigns ICD codes to clinical notes, but it remains challenging due to long documents, imbalanced label distributions, and diverse terms. These challenges are especially sever...
arXiv:2606. 31577v1 Announce Type: cross Abstract: Conformal predictions have attracted significant attention in the field of uncertainty quantification, mainly because of their strong marginal coverage guarantees.