1.0Microsoft Researchhttps://www.microsoft.com/en-us/researchRalf Herbrichhttps://www.microsoft.com/en-us/research/people/rherb/Algorithmic Luckiness - Microsoft Researchrich600338<blockquote class="wp-embedded-content" data-secret="okmiydJNj7"><a href="https://www.microsoft.com/en-us/research/publication/algorithmic-luckiness-2/">Algorithmic Luckiness</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/algorithmic-luckiness-2/embed/#?secret=okmiydJNj7" width="600" height="338" title="“Algorithmic Luckiness” — Microsoft Research" data-secret="okmiydJNj7" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script>
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Classical statistical learning theory studies the generalisation performance of machine learning algorithms rather indirectly. One of the main detours is that algorithms are studied in terms of the hypothesis class that they draw their hypotheses from. In this paper, motivated by the luckiness framework of Shawe-Taylor et al. (1998), we study learning algorithms more directly […]