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Functions of the ratio of the densities $p/q$ are widely used in machine learning to quantify the discrepancy between the two distributions $p$ and $q$.
An information-theoretic inequality and its application to the evidence of the ergodicity of markoff’s chains
Imre Csiszár · 1964
Earlier work this paper cites.
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Featurized density ratio estimation
Kristy Choi, Madeline Liao, and Stefano Ermon
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Density ratio estimation via infinitesimal classification
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Non-negative bregman divergence minimization for deep direct density ratio estimation
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Analyzing and improving the optimization landscape of noise-contrastive estimation
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