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Sparse Gaussian Processes with Spherical Harmonic Features.

, , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 2793-2802. PMLR, (2020)

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Deep Neural Networks as Point Estimates for Deep Gaussian Processes., , , , , and . NeurIPS, page 9443-9455. (2021)Scalable Thompson Sampling using Sparse Gaussian Process Models., , , , and . NeurIPS, page 5631-5643. (2021)Deep Gaussian Process metamodeling of sequentially sampled non-stationary response surfaces., , , , and . WSC, page 1728-1739. IEEE, (2017)Deep Gaussian Processes with Importance-Weighted Variational Inference., , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 5589-5598. PMLR, (2019)A Tutorial on Sparse Gaussian Processes and Variational Inference., , , and . CoRR, (2020)The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs., , , , , , , , and . CoRR, (2024)Translation Insensitivity for Deep Convolutional Gaussian Processes, , , , and . (2019)cite arxiv:1902.05888.Sparse Gaussian Processes with Spherical Harmonic Features., , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 2793-2802. PMLR, (2020)Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes., , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 34143-34160. PMLR, (2023)Neural Diffusion Processes., , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 8990-9012. PMLR, (2023)