most citedSLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives

2 citations · 2 across the 1 of their papers we have counts for

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1 paper

cs.LG2020★ 2 cited

SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives

Emmanouil Angelis, Philippe Wenk, Bernhard Schölkopf +2

Gaussian processes are an important regression tool with excellent analytic properties which allow for direct integration of derivative observations. However, vanilla GP methods sc…