gaussian process

Non-Gaussian Tensor Programs NeurIPS 2022 Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition ICML 2022 Nonparametric Sparse Tensor Factorization with Hierarchical Gamma Processes ICML 2022 Non-separable Spatio-temporal Graph Kernels via SPDEs AISTATS 2022 Nonstationary multi-output Gaussian processes via harmonizable spectral mixtures AISTATS 2022 On provably robust meta-Bayesian optimization UAI 2022 On the inability of Gaussian process regression to optimally learn compositional functions NeurIPS 2022 On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels NeurIPS 2022 On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization AISTATS 2022 Parallel MCMC Without Embarrassing Failures AISTATS 2022 Physics Informed Deep Kernel Learning AISTATS 2022 Posterior and Computational Uncertainty in Gaussian Processes NeurIPS 2022 Preconditioning for Scalable Gaussian Process Hyperparameter Optimization ICML 2022 Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations AISTATS 2022 Rate of Convergence of Polynomial Networks to Gaussian Processes COLT 2022 Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization AISTATS 2022 Revisiting Active Sets for Gaussian Process Decoders NeurIPS 2022 Sample-Then-Optimize Batch Neural Thompson Sampling NeurIPS 2022 Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation ICML 2022 Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times ICML 2022 Sensing Cox Processes via Posterior Sampling and Positive Bases AISTATS 2022 Sparse Gaussian Process Hyperparameters: Optimize or Integrate? NeurIPS 2022 Spatial Multivariate Trees for Big Data Bayesian Regression JMLR 2022 Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty ICML 2022 Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport NeurIPS 2022