Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime

Neural Information Processing Systems 

In this work, we unify and extend this line of work, providing characterization of all regimes and excess error decay rates that can be observed in terms of the interplay of noise and regularization. In particular, we show the existence of a transition in the noisy setting between the noiseless exponents to its noisy values as the sample complexity is increased.

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