The content delves into the foundations of generalization bounds, covering information-theoretic and PAC-Bayesian approaches. It discusses the connection between generalization and information theory, tools for deriving bounds, and applications in various learning models. The structure includes an introduction, foundations, tools, generalization bounds in expectation and probability, the CMI framework, applications, and concluding remarks.
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arxiv.org
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