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Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds

NeurIPS 2023 · Probabilistic and scientific modeling › Uncertainty and Gaussian processes · approach: PAC-Bayes and decoupled GP learning · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use an extrinsic Matérn GP instead of an intrinsic Matérn GPMatérn GP regression on compact Riemannian manifolds · Compact Riemannian manifoldsposterior contraction rate: n^{-\tfrac{2\min(\beta,\nu)}{2\nu+d}}No clear effect
Truncate the intrinsic Karhunen–Loève expansion at the stated growing level (ablation)Intrinsic Matérn GP · Compact Riemannian manifoldsposterior contraction rate: n^{-\tfrac{2\min(\beta,\nu)}{2\nu+d}}No clear effect
Use an intrinsic rather than an extrinsic Matérn GPMatérn GP regression · Dumbbell manifoldpointwise worst-case expected errorHelped
Use an intrinsic rather than an extrinsic Matérn GPMatérn GP regression · Dragon manifoldpointwise worst-case expected errorHelped
Use an intrinsic rather than an extrinsic Matérn GPMatérn GP regression · Spherepointwise worst-case expected errorNo clear effect

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