Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …

Online reviews are often the primary factor in a customer's decision to purchase a product or
service, and are a valuable source of information that can be used to determine public …

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 …

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 …

We study the problem of learning conditional generators from noisy labeled samples, where
the labels are corrupted by random noise. A standard training of conditional GANs will not …

Learning with noisy labels is a common challenge in supervised learning. Existing
approaches often require practitioners to specify noise rates, ie, a set of parameters …

Plant‐pathogenic fungi secrete effector proteins to facilitate infection. We describe extensive
improvements to EffectorP, the first machine learning classifier for fungal effector prediction …

Urban land use information plays an essential role in a wide variety of urban planning and
environmental monitoring processes. During the past few decades, with the rapid …

Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

In this paper, we theoretically study the problem of binary classification in the presence of
random classification noise---the learner, instead of seeing the true labels, sees labels that …