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Scale invariant process regression: Towards Bayesian ML with minimal assumptions. (arXiv:2208.10461v3 [stat.ML] UPDATED)
Nov. 1, 2022, 1:12 a.m. | Matthias Wieler
cs.LG updates on arXiv.org arxiv.org
Current methods for regularization in machine learning require quite specific
model assumptions (e.g. a kernel shape) that are not derived from prior
knowledge about the application, but must be imposed merely to make the method
work. We show in this paper that regularization can indeed be achieved by
assuming nothing but invariance principles (w.r.t. scaling, translation, and
rotation of input and output space) and the degree of differentiability of the
true function.
Concretely, we derive a novel (non-Gaussian) stochastic process …
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