Fast learning rates with heavy-tailed losses
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We study fast learning rates when the losses are not necessarily bounded and may have a distribution with heavy tails. To enable such analyses, we introduce two new conditions: (i) the envelope function _f ∈F|ℓ∘ f|, where ℓ is the loss function and F is the hypothesis class, exists and is L^r-integrable, and (ii) ℓ satisfies the multi-scale Bernstein's condition on F. Under these assumptions, we prove that learning rate faster than O(n^-1/2) can be obtained and, depending on r and the multi-scale Bernstein's powers, can be arbitrarily close to O(n^-1). We then verify these assumptions and derive fast learning rates for the problem of vector quantization by k-means clustering with heavy-tailed distributions. The analyses enable us to obtain novel learning rates that extend and complement existing results in the literature from both theoretical and practical viewpoints.
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