Some notes concerning a generalized KMM-type optimization method for density ratio estimation
This work addresses density ratio estimation for machine learning applications, but it appears incremental as it builds on the existing KMM method.
The paper tackles the problem of density ratio estimation by extending the KMM method with a new loss function to handle more general cases involving subsets of training and test data, resulting in new optimization algorithms.
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.