CUTS-GPR performs numerically exact Gaussian process regression with near-linear scaling in training points N and low-order polynomial scaling in dimensions D by exploiting additive kernels on incomplete grids.
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Non-Hermitian systems admit equivalent descriptions in isomorphic Hilbert spaces related by Krein metrics, with physical quantities transported accordingly; illustrated on a two-level spin model via Robertson uncertainty relation as consistency test.
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Don't Get Your Kroneckers in a Twist: Gaussian Processes on High-Dimensional Incomplete Grids
CUTS-GPR performs numerically exact Gaussian process regression with near-linear scaling in training points N and low-order polynomial scaling in dimensions D by exploiting additive kernels on incomplete grids.