Learning from Uncertainty-dependent Missing Labels for Semi-supervised Classification
Read the original on arXiv Statistics ML →The Flow has not summarised this story yet — read it at arXiv Statistics ML.
The Flow has not summarised this story yet — read it at arXiv Statistics ML.
arXiv:2608.30561v1 Announce Type: cross Abstract: Informative label missingness can change the usual efficiency ordering between completely and partially labelled classifiers because the pattern of m...
arXiv:2609.00774v1 Announce Type: cross Abstract: We consider semi-supervised classification from a partially classified sample arising from a two-component Weibull mixture. The feature is observed f...
arXiv:2609.06873v1 Announce Type: cross Abstract: We study how a limited labeling budget should be allocated to minimize multiclass zero-one classification risk. We consider parametric classification...
arXiv:2511. 22823v2 Announce Type: replace-cross Abstract: Weakly supervised learning has emerged as a practical alternative to fully supervised learning when complete and accurate labels are costly or infeasible to acquire.
arXiv:2512. 03322v4 Announce Type: replace-cross Abstract: Partially labelled samples arise when features are observed for all data, but class labels are available for only a subset.
arXiv:2601. 11670v3 Announce Type: replace-cross Abstract: Pseudo-label selection in semi-supervised learning is commonly driven by maximum-confidence thresholds, yet confidence alone can be unreliable under model overconfidence and class imbalance.