核心概念
Information-theoretic and PAC-Bayesian perspectives provide insights into generalization bounds in machine learning.
摘要
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.
統計資料
arXiv:2309.04381v2 [cs.LG] 27 Mar 2024
引述
"In this monograph, we highlight this strong connection and present a unified treatment of PAC-Bayesian and information-theoretic generalization bounds."
"Information-theoretic generalization bounds make this intuition precise by characterizing the generalization error of (randomized) learning algorithms in terms of information-theoretic metrics."