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Found 5 papers, 1 papers with code
Information-Theoretic Generalization Bounds for Deep Neural Networks
This enables refining our generalization bounds to capture the contraction as a function of the network architecture parameters.
How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm?
Our findings offer new insights that the generalization performance of SSL with pseudo-labeling is affected not only by the information between the output hypothesis and input training data but also by the information {\em shared} between the {\em labeled} and {\em pseudo-labeled} data samples.
Information-Theoretic Characterization of the Generalization Error for Iterative Semi-Supervised Learning
Using information-theoretic principles, we consider the generalization error (gen-error) of iterative semi-supervised learning (SSL) algorithms that iteratively generate pseudo-labels for a large amount of unlabelled data to progressively refine the model parameters.
Information-Theoretic Generalization Bounds for Iterative Semi-Supervised Learning
We consider iterative semi-supervised learning (SSL) algorithms that iteratively generate pseudo-labels for a large amount unlabelled data to progressively refine the model parameters.
Optimal Change-Point Detection with Training Sequences in the Large and Moderate Deviations Regimes
This paper investigates a novel offline change-point detection problem from an information-theoretic perspective.
