COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
The Journal of Machine Learning Research
Pac-bayesian generalisation error bounds for gaussian process classification
The Journal of Machine Learning Research
Tutorial on Practical Prediction Theory for Classification
The Journal of Machine Learning Research
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
PAC-Bayesian learning of linear classifiers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A discriminative model for semi-supervised learning
Journal of the ACM (JACM)
COLT'07 Proceedings of the 20th annual conference on Learning theory
Structural risk minimization over data-dependent hierarchies
IEEE Transactions on Information Theory
PAC-Bayesian Analysis of Co-clustering and Beyond
The Journal of Machine Learning Research
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We develop the idea that the PAC-Bayes prior can be informed by the data-generating distribution. We prove sharp bounds for an existing framework, and develop insights into function class complexity in this model and suggest means of controlling it with new algorithms. In particular we consider controlling capacity with respect to the unknown geometry of the data-generating distribution. We finally extend this localization to more practical learning methods.