Outlier detection has been a topic in statistics for centuries. Over mainly the last two
decades, there has been also an increasing interest in the database and data mining …

Estimation of the ratio of probability densities has attracted a great deal of attention since it
can be used for addressing various statistical paradigms. A naive approach to density-ratio …

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 …

Learning a classifier from positive and unlabeled data is an important class of classification
problems that are conceivable in many practical applications. In this paper, we first show that …

Domain adaptation allows knowledge from a source domain to be transferred to a different
but related target domain. Intuitively, discovering a good feature representation across …

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

Theory, algorithms, and applications of machine learning techniques to overcome" covariate
shift" non-stationarity. As the power of computing has grown over the past few decades, the …

We address the problem of estimating the ratio of two probability density functions, which is
often referred to as the importance. The importance values can be used for various …

We propose a new statistical approach to the problem of inlier-based outlier detection, ie,
finding outliers in the test set based on the training set consisting only of inliers. Our key idea …

It has been shown that unsupervised outlier detection methods can be adapted to the one-
class classification problem (Janssens and Postma, in: Proceedings of the 18th annual …