May 16, 2024, 4:42 a.m. | Luca Ambrogioni

cs.LG updates on

arXiv:2310.02877v2 Announce Type: replace-cross
Abstract: The behavior of a GP regression depends on the choice of covariance function. Stationary covariance functions are preferred in machine learning applications. However, (non-periodic) stationary covariance functions are always mean reverting and can therefore exhibit pathological behavior when applied to data that does not relax to a fixed global mean value. In this paper we show that it is possible to use improper GP priors with infinite variance to define processes that are stationary but …

abstract applications arxiv behavior covariance cs.lg data function functions gaussian processes however machine machine learning machine learning applications mean processes regression replace type

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