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Gaussian Processes

570 papers

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Papers

Error bounds for any regression model using Gaussian processes with gradient information AISTATS 2024
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels AISTATS 2024
Intrinsic Gaussian Vector Fields on Manifolds AISTATS 2024
A Unifying Variational Framework for Gaussian Process Motion Planning AISTATS 2024
Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning AISTATS 2024
Variational Gaussian Process Diffusion Processes AISTATS 2024
Scalable Meta-Learning with Gaussian Processes AISTATS 2024
Large-Scale Gaussian Processes via Alternating Projection AISTATS 2024
Trigonometric Quadrature Fourier Features for Scalable Gaussian Process Regression AISTATS 2024
Hodge-Compositional Edge Gaussian Processes AISTATS 2024
Fast Fourier Bayesian Quadrature AISTATS 2024
Bayesian Exploration of Pre-trained Models for Low-shot Image Classification CVPR 2024
Symplectic Neural Gaussian Processes for Meta-learning Hamiltonian Dynamics IJCAI 2024
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees JMLR 2024
Kernel Thinning JMLR 2024
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case JMLR 2024
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces JMLR 2024
Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC JMLR 2024
Transfer learning for tensor Gaussian graphical models JMLR 2024
Failure-Aware Gaussian Process Optimization with Regret Bounds NIPS 2023
Conditional Matrix Flows for Gaussian Graphical Models NIPS 2023
Contextual Gaussian Process Bandits with Neural Networks NIPS 2023
Variational Gaussian processes for linear inverse problems NIPS 2023
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds NIPS 2023
Sharp Calibrated Gaussian Processes NIPS 2023