PAC-bayesian learning
PulseAugur coverage of PAC-bayesian learning — every cluster mentioning PAC-bayesian learning across labs, papers, and developer communities, ranked by signal.
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PAC-Bayes framework offers new approach to learning system controllers
Researchers have developed a new PAC-Bayes framework designed to learn controllers for unknown stochastic linear discrete-time systems. This framework provides a data-dependent, high-probability bound on the performance…
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New PAC-Bayesian Framework Quantifies Uncertainty in Test-Time Adaptation
Researchers have developed a PAC-Bayesian framework to quantify epistemic uncertainty in test-time adaptation (TTA) methods. This framework uses maximum mean discrepancy (MMD) between source and target distributions to …
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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-…