Journal of the ACM (JACM)
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
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Graph Laplacians and their Convergence on Random Neighborhood Graphs
The Journal of Machine Learning Research
Support Vector Machines
A discriminative model for semi-supervised learning
Journal of the ACM (JACM)
COLT'07 Proceedings of the 20th annual conference on Learning theory
Functional Bregman Divergence and Bayesian Estimation of Distributions
IEEE Transactions on Information Theory
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We further develop the idea that the PAC-Bayes prior can be informed by the data-generating distribution. We use this framework to prove sharp risk bounds for stochastic exponential weights algorithms, and develop insights into controlling function class complexity in this method. In particular we consider controlling capacity with respect to the unknown geometry defined by the data-generating distribution. We also use the method to obtain new bounds for RKHS regularization schemes such as SVMs.