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 …

The year 2015 marks the 30th birthday of DC (Difference of Convex functions) programming
and DCA (DC Algorithms) which constitute the backbone of nonconvex programming and …

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 develop and analyze M-estimation methods for divergence functionals and the
likelihood ratios of two probability distributions. Our method is based on a nonasymptotic …

The general perception is that kernel methods are not scalable, so neural nets become the
choice for large-scale nonlinear learning problems. Have we tried hard enough for kernel …

A common setting for novelty detection assumes that labeled examples from the nominal
class are available, but that labeled examples of novelties are unavailable. The standard …

Detecting instances of unknown categories is an important task for a multitude of problems
such as object recognition, event detection, and defect localization. This article investigates …

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 …