<ul><li>This paper provides a unified perspective for the Kullback-Leibler (KL)-divergence and the integral probability metrics (IPMs) from the perspective of maximum likelihood density-ratio estimation.</li><li>The paper shows that the KL-divergence and IPMs can be represented as maximal likelihoods, differing only by sampling schemes.</li><li>The paper also introduces a new class of probability divergences called the Density Ratio Metrics (DRMs), which interpolate the KL-divergence and IPMs.</li><li>The authors propose a new adversarial training scheme for learning fair representation (LFR) with a theoretical guarantee of fairness for the final prediction model.</li></ul>

How Integral Probability Metric works part4(Machine Learning 2024)

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