Learnability with Partial Labels and Adaptive Nearest Neighbors
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866918399764856832 |
|---|---|
| author | Errandonea, Nicolas A. Mazuelas, Santiago Lozano, Jose A. Dasgupta, Sanjoy |
| author_facet | Errandonea, Nicolas A. Mazuelas, Santiago Lozano, Jose A. Dasgupta, Sanjoy |
| contents | Prior work on partial labels learning (PLL) has shown that learning is possible even when each instance is associated with a bag of labels, rather than a single accurate but costly label. However, the necessary conditions for learning with partial labels remain unclear, and existing PLL methods are effective only in specific scenarios. In this work, we mathematically characterize the settings in which PLL is feasible. In addition, we present PL A-$k$NN, an adaptive nearest-neighbors algorithm for PLL that is effective in general scenarios and enjoys strong performance guarantees. Experimental results corroborate that PL A-$k$NN can outperform state-of-the-art methods in general PLL scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15781 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learnability with Partial Labels and Adaptive Nearest Neighbors Errandonea, Nicolas A. Mazuelas, Santiago Lozano, Jose A. Dasgupta, Sanjoy Machine Learning Prior work on partial labels learning (PLL) has shown that learning is possible even when each instance is associated with a bag of labels, rather than a single accurate but costly label. However, the necessary conditions for learning with partial labels remain unclear, and existing PLL methods are effective only in specific scenarios. In this work, we mathematically characterize the settings in which PLL is feasible. In addition, we present PL A-$k$NN, an adaptive nearest-neighbors algorithm for PLL that is effective in general scenarios and enjoys strong performance guarantees. Experimental results corroborate that PL A-$k$NN can outperform state-of-the-art methods in general PLL scenarios. |
| title | Learnability with Partial Labels and Adaptive Nearest Neighbors |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.15781 |