In the present paper we introduce new optimization algorithms for the task of density ratio
estimation. More precisely, we consider extending the well-known KMM method using the …

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 …

Density ratio estimation has a broad application in the world of machine learning and data
science, especially in transfer learning and contrastive learning. This work mainly focuses …

Optimal cross-validation in density estimation with the L2-loss Page 1 The Annals of Statistics
2014, Vol. 42, No. 5, 1879–1910 DOI: 10.1214/14-AOS1240 © Institute of Mathematical …

We develop two density ratio estimation (DRE) methods with robustness to outliers. These
are based on the divergence with a weight function to weaken the adverse effects of outliers …

Estimating truncated density models is difficult, as these models have intractable
normalising constants and hard to satisfy boundary conditions. Score matching can be …

In this study, the computational properties of a kernel-based least-squares density-ratio
estimator are investigated from the viewpoint of condition numbers. The condition number of …

In this paper, we propose a density estimation algorithm called\textit {Gradient Boosting
Histogram Transform}(GBHT), where we adopt the\textit {Negative Log Likelihood} as the …

We introduce a general constructive setting of the density ratio estimation problem as a
solution of a (multidimensional) integral equation. In this equation, not only its right hand …

This paper studies V-fold cross-validation for model selection in least-squares density
estimation. The goal is to provide theoretical grounds for choosing V in order to minimize the …