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.

翻译：本文针对密度比估计任务提出了新的优化算法。具体而言，我们考虑通过构造合适的损失函数来扩展著名的KMM方法，从而涵盖更一般的情形，即分别针对训练数据和测试数据的子集进行密度比估计。相关代码可在https://github.com/CDAlecsa/Generalized-KMM获取。