PAC-Bayes Mini-tutorial: A Continuous Union Bound
PAC-Bayes Mini-tutorial: A Continuous Union Bound
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PAC-贝叶斯迷你教程：连续并集
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
T. Erven
中科院分区：
文献类型：
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作者：
T. Erven
当我第一次遇到Pac-Bayesian的集中不平等时，他们似乎与Hoeffding's和Bernstein的不平等等老式的结果脱节。但是，至少对于Pac-Bayesian边界的一种风味，实际上存在非常紧密的关系，主要创新是联合界限的连续版本，以及一些巧妙的应用。这是从机器学习的角度提出的要点。
When I first encountered PAC-Bayesian concentration inequalities they seemed to me to be rather disconnected from good old-fashioned results like Hoeffding's and Bernstein's inequalities. But, at least for one flavour of the PAC-Bayesian bounds, there is actually a very close relation, and the main innovation is a continuous version of the union bound, along with some ingenious applications. Here's the gist of what's going on, presented from a machine learning perspective.