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Found 9 papers, 5 papers with code
Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets
We propose data-dependent uniform generalization bounds by approaching the problem from a PAC-Bayesian perspective.
Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures
In statistical learning theory, a generalization bound usually involves a complexity measure imposed by the considered theoretical framework.
A PAC-Bayesian Link Between Generalisation and Flat Minima
Modern machine learning usually involves predictors in the overparametrised setting (number of trained parameters greater than dataset size), and their training yield not only good performances on training data, but also good generalisation capacity.
Tighter Generalisation Bounds via Interpolation
We also instantiate our bounds as training objectives, yielding non-trivial guarantees and practical performances.
From Mutual Information to Expected Dynamics: New Generalization Bounds for Heavy-Tailed SGD
This has been successfully applied to generalization theory by exploiting the fractal properties of those dynamics.
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound
We investigate a stochastic counterpart of majority votes over finite ensembles of classifiers, and study its generalization properties.
Self-Bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound
In the PAC-Bayesian literature, the C-Bound refers to an insightful relation between the risk of a majority vote classifier (under the zero-one loss) and the first two moments of its margin (i. e., the expected margin and the voters' diversity).
A PAC-Bayes Analysis of Adversarial Robustness
We propose the first general PAC-Bayesian generalization bounds for adversarial robustness, that estimate, at test time, how much a model will be invariant to imperceptible perturbations in the input.
A General Framework for the Practical Disintegration of PAC-Bayesian Bounds
PAC-Bayesian bounds are known to be tight and informative when studying the generalization ability of randomized classifiers.
