Nov 8, 2023 · We develop generalization bounds for transductive learning algorithms in the context of information theory and PAC-Bayesian theory.
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It is shown that the transductive generalization gap can be bounded by the mutual information between training labels selection and the hypothesis, ...
We develop generalization bounds for transductive learning algorithms in the context of information theory and PAC-Bayesian theory, covering both the random ...
Nov 9, 2021 · We derive information-theoretic generalization bounds for supervised learning algorithms based on the information contained in predictions.
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Jun 10, 2024 · The paper develops generalization bounds for transductive learning algorithms using information theory and PAC-Bayesian theory.
We derive information-theoretic generalization bounds for supervised learning algorithms based on the information contained in predictions.
Missing: Transductive Applications.
This paper presents a general methodology for deriving information-theoretic gener- alization bounds for learning algorithms. The main technical tool is a ...
Information-Theoretic Generalization Bounds for Transductive Learning and its Applications · Huayi TangYong Liu. Computer Science, Mathematics. arXiv.org. 2023.
We develop generalization bounds for transductive learning algorithms in the context of information theory and PAC-Bayesian theory, covering both the random ...
Abstract. In this work, we present a variety of novel information-theoretic generalization bounds for learning algorithms, from the supersample setting.
Missing: Transductive | Show results with:Transductive