<ul><li>The paper discusses the use of Gibbs measures in machine learning research.</li><li>It focuses on the shortcomings of Bayes posteriors as inferential devices and proposes targeting a Gibbs measure based on losses.</li><li>The paper introduces the concept of using pseudo-observations to estimate the losses in situations where they are not analytically available.</li><li>The findings highlight the importance of the number of pseudo-observations in accurately approximating the exact Gibbs measure.</li></ul>

Applications of Gibbs measure in Machine Learning research part1

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