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Random walk kernels and learning curves for Gaussian process regression on random graphs
Our method for predicting the learning curves using belief propagation is significantly more accurate than previous approximations and should become exact in the limit of large random graphs.
Exact learning curves for Gaussian process regression on large random graphs
We study learning curves for Gaussian process regression which characterise performance in terms of the Bayes error averaged over datasets of a given size.
Kernels and learning curves for Gaussian process regression on random graphs
The fully correlated limit is reached only once loops become relevant, and we estimate where the crossover to this regime occurs.
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