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 …

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 …

Implicit models, which allow for the generation of samples but not for point-wise evaluation
of probabilities, are omnipresent in real-world problems tackled by machine learning and a …

Methods for directly estimating the ratio of two probability density functions have been
actively explored recently since they can be used for various data processing tasks such as …

The ratio of two probability densities can be used for solving various machine learning tasks
such as covariate shift adaptation (importance sampling), outlier detection (likelihood-ratio …

We propose a novel approximate inference algorithm that approximates a target distribution
by amortising the dynamics of a user-selected MCMC sampler. The idea is to initialise …

Egocentric cameras can be used to benefit such tasks as analyzing fine motor skills,
recognizing gestures and learning about hand-object manipulation. To enable such …

Covariate shift generalization, a typical case in out-of-distribution (OOD) generalization,
requires a good performance on the unknown testing distribution, which varies from the …

Recently, a new general framework of statistical data processing based on the ratio of
probability densities has been developed (Sugiyama et al.. 2009: Sugiyania et al.. 2011) …

Density ratio estimation has gathered a great deal of attention recently since it can be used
for various data processing tasks. In this paper, we consider three methods of density ratio …