arXiv Machine Learning July 15, 2026 By Wei Tang, Yin-Fang Yang, Weijia Zhang, Min-Ling Zhang

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning

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arXiv:2512. 17788v2 Announce Type: replace Abstract: Multi-instance partial-label learning (MIPL) is a weakly supervised framework that extends the principles of multi-instance learning (MIL) and partial-label learning (PLL) to address the challenges of inexact supervision in both instance and label spaces.

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.

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