The following pages link to (Q3093326):
Displaying 50 items.
- Vecchia-approximated Deep Gaussian Processes for Computer Experiments (Q84900) (← links)
- Hilbert space methods for reduced-rank Gaussian process regression (Q91877) (← links)
- Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming (Q91882) (← links)
- Compression, inversion, and approximate PCA of dense kernel matrices at near-linear computational complexity (Q92247) (← links)
- A case study of the widely applicable Bayesian information criterion and its optimality (Q261023) (← links)
- A new expected-improvement algorithm for continuous minimax optimization (Q280101) (← links)
- GP-DEMO: differential evolution for multiobjective optimization based on Gaussian process models (Q319093) (← links)
- Computationally efficient algorithm for Gaussian process regression in case of structured samples (Q327224) (← links)
- Variational inference for sparse spectrum Gaussian process regression (Q341145) (← links)
- Efficient cross-validation for kernelized least-squares regression with sparse basis expansions (Q439007) (← links)
- Real-time model learning using incremental sparse spectrum Gaussian process regression (Q461107) (← links)
- Kernel methods in system identification, machine learning and function estimation: a survey (Q462325) (← links)
- Online model regression for nonlinear time-varying manufacturing systems (Q518306) (← links)
- Vecchia-Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data (Q830598) (← links)
- Laplace approximation for logistic Gaussian process density estimation and regression (Q899031) (← links)
- CSI: a nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data (Q906224) (← links)
- A scalable preference model for autonomous decision-making (Q1621876) (← links)
- Variational Hamiltonian Monte Carlo via score matching (Q1631559) (← links)
- Merging MCMC subposteriors through Gaussian-process approximations (Q1631561) (← links)
- Bayesian inference for conditional copulas using Gaussian process single index models (Q1662326) (← links)
- A novel probabilistic approach for vehicle position prediction in free, partial, and full GPS outages (Q1664919) (← links)
- Many regression algorithms, one unified model: a review (Q1669152) (← links)
- Low-rank decomposition meets kernel learning: a generalized Nyström method (Q1680675) (← links)
- Large scale variable fidelity surrogate modeling (Q1680849) (← links)
- Reduced-space Gaussian process regression for data-driven probabilistic forecast of chaotic dynamical systems (Q1691147) (← links)
- Hamiltonian Monte Carlo acceleration using surrogate functions with random bases (Q1703832) (← links)
- Stochastic variational hierarchical mixture of sparse Gaussian processes for regression (Q1722733) (← links)
- A joint Gaussian process model for active visual recognition with expertise estimation in crowdsourcing (Q1800023) (← links)
- An efficient algorithm for learning to rank from preference graphs (Q1959647) (← links)
- Geometric deep learning for computational mechanics. I: Anisotropic hyperelasticity (Q2021107) (← links)
- Systematic sensor placement for structural anomaly detection in the absence of damaged states (Q2021134) (← links)
- An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques (Q2024169) (← links)
- A new type of conditioning of stationary fields and its application to the spectral simulation approach in geostatistics (Q2040708) (← links)
- Large scale multi-label learning using Gaussian processes (Q2051297) (← links)
- Locally induced Gaussian processes for large-scale simulation experiments (Q2058747) (← links)
- Deep state-space Gaussian processes (Q2058900) (← links)
- Gaussian processes with skewed Laplace spectral mixture kernels for long-term forecasting (Q2071359) (← links)
- A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with skew Gaussian processes (Q2071487) (← links)
- Degenerate Gaussian factors for probabilistic inference (Q2077023) (← links)
- Uncertainty quantification of a computer model for binary black hole formation (Q2078270) (← links)
- Mapping interstellar dust with Gaussian processes (Q2080783) (← links)
- Alpha-divergence minimization for deep Gaussian processes (Q2092453) (← links)
- Multi-scale Vecchia approximations of Gaussian processes (Q2102959) (← links)
- An efficient implementation for spatial-temporal Gaussian process regression and its applications (Q2103652) (← links)
- Variational inference with vine copulas: an efficient approach for Bayesian computer model calibration (Q2110192) (← links)
- Solving and learning nonlinear PDEs with Gaussian processes (Q2133484) (← links)
- Stochastic embeddings of dynamical phenomena through variational autoencoders (Q2133707) (← links)
- Variational Bayesian approximation of inverse problems using sparse precision matrices (Q2138759) (← links)
- Nested aggregation of experts using inducing points for approximated Gaussian process regression (Q2163216) (← links)
- A Gaussian process regression approach within a data-driven POD framework for engineering problems in fluid dynamics (Q2167597) (← links)
