Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Partial label learning (PLL) is an emerging framework in weakly supervised machine
learning with broad application prospects. It handles the case in which each training …

A prominent technique for self-supervised representation learning has been to contrast
semantically similar and dissimilar pairs of samples. Without access to labels, dissimilar …

Deep Learning with noisy labels is a practically challenging problem in weakly-supervised
learning. The state-of-the-art approaches" Decoupling" and" Co-teaching+" claim that the" …

Learning from positive and unlabeled data or PU learning is the setting where a learner only
has access to positive examples and unlabeled data. The assumption is that the unlabeled …

In many applications of classifier learning, training data suffers from label noise. Deep
networks are learned using huge training data where the problem of noisy labels is …

From only positive (P) and unlabeled (U) data, a binary classifier could be trained with PU
learning, in which the state of the art is unbiased PU learning. However, if its model is very …

Partial-label learning (PLL) is a multi-class classification problem, where each training
example is associated with a set of candidate labels. Even though many practical PLL …

We discuss binary classification from only from positive and unlabeled data (PU
classification), which is conceivable in various real-world machine learning problems. Since …

We introduce the problem of domain adaptation under Open Set Label Shift (OSLS), where
the label distribution can change arbitrarily and a new class may arrive during deployment …