This paper proposes a PAC-Bayes bound to measure the performance of Support Vector Machine (SVM) classifiers. The bound is based on learning a prior over the ...
This paper proposes a PAC-Bayes bound to measure the performance of Support. Vector Machine (SVM) classifiers. The bound is based on learning a prior over.
Nov 24, 2022 · We use these tight bounds to better understand the role of model size, equivariance, and the implicit biases of optimization, for generalization ...
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Feb 12, 2023 · In this paper, we show that we can do even better: we show how to refine the proof strategy of the PAC-Bayes bounds and achieve \emph{even tighter} guarantees.
In this paper, we show that we can do even better: we show how to refine the proof strategy of the PAC-Bayes bounds and achieve even tighter guarantees. Our ...
Oct 31, 2022 · We propose state-of-the-art PAC-Bayes compression bounds and use them to understand generalization in deep learning.
This paper proposes a PAC-Bayes bound to measure the performance of Support. Vector Machine (SVM) classifiers. The bound is based on learning a prior over.
We introduce a modified version of the excess risk, which can be used to obtain empirically tighter, faster-rate PAC-Bayesian generalisation bounds.
This paper proposes a PAC-Bayes bound to measure the performance of Support Vector Machine (SVM) classifiers. The bound is based on learning a prior over ...
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This repository hosts the code for PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization by Sanae Lotfi*, Marc Finzi*, Sanyam Kapoor*, ...
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