Some notes concerning a generalized KMM-type optimization method for density ratio estimation

TL;DR

This paper introduces generalized optimization algorithms for density ratio estimation, extending the KMM method to handle more complex scenarios involving subsets of training and test data.

Contribution

It presents new algorithms that extend the KMM method using a novel loss function for broader density ratio estimation applications.

Findings

Developed algorithms that generalize KMM for complex data scenarios.

Provided implementation code for the proposed methods.

Enhanced density ratio estimation accuracy in generalized settings.

Abstract

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 construction of a suitable loss function, in order to encompass more general situations involving the estimation of density ratio with respect to subsets of the training data and test data, respectively. The associated codes can be found at https://github.com/CDAlecsa/Generalized-KMM.