PAC-bayesian learning
PulseAugur coverage of PAC-bayesian learning — every cluster mentioning PAC-bayesian learning across labs, papers, and developer communities, ranked by signal.
3 天有情绪数据
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PAC-Bayes框架为学习系统控制器提供新方法
研究人员开发了一个新的PAC-Bayes框架，旨在学习未知随机线性离散时间系统的控制器。该框架为任何学习到的控制器提供了数据依赖的、高概率的性能界限。所提出的算法提供了理论保证，并适用于有限和无限控制器空间，数值结果表明在特定场景下性能可与LQG相媲美。
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新的 PAC-贝叶斯框架量化测试时自适应中的不确定性
研究人员开发了一个 PAC-贝叶斯框架，用于量化测试时自适应 (TTA) 方法中的认知不确定性。该框架使用源分布和目标分布之间的最大均值差异 (MMD) 来推导泛化界。通过将 MMD-balls 解释为 credal sets，该方法将认知不确定性与偶然不确定性分开，提供了一种有原则的方法来决定何时自适应是有益的。
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New PAC-Bayes Framework for Controlling Unknown Linear Systems
This paper introduces a PAC-Bayes framework designed to learn controllers for unknown stochastic linear discrete-time systems. The research provides a data-dependent bound on controller performance and proposes new lear…
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PAC-Bayesian analysis bounds wireless inference degradation in edge learning
Researchers have developed a theoretical framework to analyze performance degradation in edge inference for neural networks operating over wireless channels. Their approach uses a PAC-Bayesian analysis to derive a high-…