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Gaussian Processes

568 directly classified papers

Papers per year

Papers

Sparse Spectral Bayesian Permanental Process with Generalized Kernel AISTATS 2023
Factorial SDE for Multi-Output Gaussian Process Regression AISTATS 2023
Optimizing Hyperparameters with Conformal Quantile Regression ICML 2023
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation AISTATS 2023
Estimating Causal Effects using a Multi-task Deep Ensemble ICML 2023
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes ICML 2023
Actually Sparse Variational Gaussian Processes AISTATS 2023
Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models ICML 2023
Mode-constrained Model-based Reinforcement Learning via Gaussian Processes AISTATS 2023
Bayesian online change point detection with Hilbert space approximate Student-t process ICML 2023
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under nonparametrized geometrical variability NIPS 2023
Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems AISTATS 2023
Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration CVPR 2023
Constrained Causal Bayesian Optimization ICML 2023
Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients ICML 2023
Gaussian Processes on Distributions based on Regularized Optimal Transport AISTATS 2023
Gaussian processes at the Helm(holtz): A more fluid model for ocean currents ICML 2023
Isotropic Gaussian Processes on Finite Spaces of Graphs AISTATS 2023
Causal Modeling of Policy Interventions From Treatment-Outcome Sequences ICML 2023
Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings AISTATS 2023
Confidence and Uncertainty Assessment for Distributional Random Forests JMLR 2023
Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priors JMLR 2023
Guided Deep Kernel Learning UAI 2023
Maximum likelihood estimation in Gaussian process regression is ill-posed JMLR 2023
A theory of representation learning gives a deep generalisation of kernel methods ICML 2023