Pages that link to "Item:Q4997432"
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The following pages link to Sparse Cholesky Factorization by Kullback--Leibler Minimization (Q4997432):
Displaying 32 items.
- Multi-scale Vecchia approximations of Gaussian processes (Q2102959) (← links)
- Do ideas have shape? Idea registration as the continuous limit of artificial neural networks (Q2111734) (← links)
- Hierarchical sparse Cholesky decomposition with applications to high-dimensional spatio-temporal filtering (Q2114043) (← links)
- Solving and learning nonlinear PDEs with Gaussian processes (Q2133484) (← links)
- Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide (Q2245146) (← links)
- Computational graph completion (Q2671739) (← links)
- (Q3140796) (← links)
- (Q3833498) (← links)
- Finite Element Representations of Gaussian Processes: Balancing Numerical and Statistical Accuracy (Q5052906) (← links)
- Scaled Vecchia Approximation for Fast Computer-Model Emulation (Q5097836) (← links)
- On Using Cholesky-Based Factorizations and Regularization for Solving Rank-Deficient Sparse Linear Least-Squares Problems (Q5350438) (← links)
- Numerical homogenization beyond scale separation (Q5887826) (← links)
- Bayesian nonstationary and nonparametric covariance estimation for large spatial data (with discussion) (Q6121621) (← links)
- Gaussian process hydrodynamics (Q6132295) (← links)
- Sparse Recovery of Elliptic Solvers from Matrix-Vector Products (Q6154205) (← links)
- Correlation-based sparse inverse Cholesky factorization for fast Gaussian-process inference (Q6172908) (← links)
- Sparse Gaussian processes for solving nonlinear PDEs (Q6173368) (← links)
- Variational Bayesian inference for CP tensor completion with subspace information (Q6180798) (← links)
- Fast macroscopic forcing method (Q6196620) (← links)
- Kernel methods are competitive for operator learning (Q6202132) (← links)
- Nearest-neighbor mixture models for non-Gaussian spatial processes (Q6203346) (← links)
- Sparse Cholesky factorization by Kullback-Leibler minimization (Q6339715) (← links)
- Spatial best linear unbiased prediction: a computational mathematics approach for high dimensional massive datasets (Q6500169) (← links)
- Learning particle swarming models from data with Gaussian processes (Q6562843) (← links)
- Scalable Bayesian Transport Maps for High-Dimensional Non-Gaussian Spatial Fields (Q6567936) (← links)
- An adaptive factorized Nyström preconditioner for regularized kernel matrices (Q6575352) (← links)
- Scalable spatio-temporal smoothing via hierarchical sparse Cholesky decomposition (Q6626523) (← links)
- Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning (Q6645133) (← links)
- Error analysis of kernel/GP methods for nonlinear and parametric PDEs (Q6648398) (← links)
- Bayesian nonparametric generative modeling of large multivariate non-Gaussian spatial fields (Q6655987) (← links)
- Implementation and analysis of GPU algorithms for Vecchia approximation (Q6657815) (← links)
- On the representation and learning of monotone triangular transport maps (Q6659499) (← links)
