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Feb, 2024
通过迭代正则化克服密度比估计中的饱和问题
Overcoming Saturation in Density Ratio Estimation by Iterated Regularization
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Lukas Gruber, Markus Holzleitner, Johannes Lehner, Sepp Hochreiter, Werner Zellinger
TL;DR
通过引入迭代正则化方法，解决了密度比估计中误差饱和问题，提高了密度比估计和深度无监督领域自适应模型增强的性能。
Abstract
Estimating the ratio of two probability densities from finitely many samples, is a central task in machine learning and statistics. In this work, we show that a large class of
kernel methods
for
density ratio estimation
→