COLT '90 Proceedings of the third annual workshop on Computational learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
PAC-Bayesian Stochastic Model Selection
Machine Learning
Estimation of mixture models
Pac-bayesian generalisation error bounds for gaussian process classification
The Journal of Machine Learning Research
Minimum complexity density estimation
IEEE Transactions on Information Theory
Information-theoretic upper and lower bounds for statistical estimation
IEEE Transactions on Information Theory
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Standard Bayesian inference can behave suboptimally if the model is wrong. We present a modification of Bayesian inference which continues to achieve good rates with wrong models. Our method adapts the Bayesian learning rate to the data, picking the rate minimizing the cumulative loss of sequential prediction by posterior randomization. Our results can also be used to adapt the learning rate in a PAC-Bayesian context. The results are based on an extension of an inequality due to T. Zhang and others to dependent random variables.