Novel Pivoted Cholesky Decompositions for Efficient Gaussian Process Inference
Fuente:
arXiv
Guardado en:
| Autores principales: | de Roos, Filip, Muratore, Fabio |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Randomly Pivoted Partial Cholesky: Random How?
por: Steinerberger, Stefan
Publicado: (2024)
por: Steinerberger, Stefan
Publicado: (2024)
The Geometry of the Pivot: A Note on Lazy Pivoted Cholesky and Farthest Point Sampling
por: Shabat, Gil
Publicado: (2026)
por: Shabat, Gil
Publicado: (2026)
Deep Gaussian Covariance Network with Trajectory Sampling for Data-Efficient Policy Search
por: Bogoclu, Can, et al.
Publicado: (2024)
por: Bogoclu, Can, et al.
Publicado: (2024)
A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications
por: Logakannan, Krishna Prasath, et al.
Publicado: (2025)
por: Logakannan, Krishna Prasath, et al.
Publicado: (2025)
Gaussian Process Boosting
por: Sigrist, Fabio
Publicado: (2020)
por: Sigrist, Fabio
Publicado: (2020)
Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inference in Large Language Models
por: Zhao, Jialin, et al.
Publicado: (2025)
por: Zhao, Jialin, et al.
Publicado: (2025)
Three Costs of Amortizing Gaussian Process Inference with Neural Processes
por: Young, Robin
Publicado: (2026)
por: Young, Robin
Publicado: (2026)
Sparse Orthogonal Variational Inference for Gaussian Processes
por: Shi, Jiaxin, et al.
Publicado: (2019)
por: Shi, Jiaxin, et al.
Publicado: (2019)
Amortized Variational Inference for Deep Gaussian Processes
por: Meng, Qiuxian, et al.
Publicado: (2024)
por: Meng, Qiuxian, et al.
Publicado: (2024)
Instrumental and Proximal Causal Inference with Gaussian Processes
por: Zhang, Yuqi, et al.
Publicado: (2026)
por: Zhang, Yuqi, et al.
Publicado: (2026)
Diffusion Bridge Variational Inference for Deep Gaussian Processes
por: Xu, Jian, et al.
Publicado: (2025)
por: Xu, Jian, et al.
Publicado: (2025)
Bayesian Causal Inference with Gaussian Process Networks
por: Giudice, Enrico, et al.
Publicado: (2024)
por: Giudice, Enrico, et al.
Publicado: (2024)
Exact Inference for Continuous-Time Gaussian Process Dynamics
por: Ensinger, Katharina, et al.
Publicado: (2023)
por: Ensinger, Katharina, et al.
Publicado: (2023)
Lightweight Gaussian Process Inference in C++ on Metal and CUDA
por: Fang, Yu-Hsueh
Publicado: (2026)
por: Fang, Yu-Hsueh
Publicado: (2026)
Sparse Cholesky Factorization for Solving Nonlinear PDEs via Gaussian Processes
por: Chen, Yifan, et al.
Publicado: (2023)
por: Chen, Yifan, et al.
Publicado: (2023)
Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference
por: Jiang, Xiaoyu, et al.
Publicado: (2024)
por: Jiang, Xiaoyu, et al.
Publicado: (2024)
On the Usage of Gaussian Process for Efficient Data Valuation
por: Bénesse, Clément, et al.
Publicado: (2025)
por: Bénesse, Clément, et al.
Publicado: (2025)
Efficient Graph Condensation via Gaussian Process
por: Wang, Lin, et al.
Publicado: (2025)
por: Wang, Lin, et al.
Publicado: (2025)
Sequential Inference for Gaussian Processes: A Signal Processing Perspective
por: Waxman, Daniel, et al.
Publicado: (2026)
por: Waxman, Daniel, et al.
Publicado: (2026)
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference
por: Wenger, Jonathan, et al.
Publicado: (2024)
por: Wenger, Jonathan, et al.
Publicado: (2024)
Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project
por: Rathore, Pratik, et al.
Publicado: (2025)
por: Rathore, Pratik, et al.
Publicado: (2025)
Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models
por: Kündig, Pascal, et al.
Publicado: (2023)
por: Kündig, Pascal, et al.
Publicado: (2023)
Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes
por: Tan, Yu, et al.
Publicado: (2025)
por: Tan, Yu, et al.
Publicado: (2025)
Variational Free Energy Pivot Selection for Pivoted Cholesky
por: Schaub, Louise, et al.
Publicado: (2026)
por: Schaub, Louise, et al.
Publicado: (2026)
Fault Detection and Identification Using a Novel Process Decomposition Algorithm for Distributed Process Monitoring
por: Villagomez, Enrique Luna, et al.
Publicado: (2024)
por: Villagomez, Enrique Luna, et al.
Publicado: (2024)
Tensor Gaussian Processes: Efficient Solvers for Nonlinear PDEs
por: Yuan, Qiwei, et al.
Publicado: (2025)
por: Yuan, Qiwei, et al.
Publicado: (2025)
LRD-MPC: Efficient MPC Inference through Low-rank Decomposition
por: Tang, Tingting, et al.
Publicado: (2026)
por: Tang, Tingting, et al.
Publicado: (2026)
Learning fast changing slow in spiking neural networks
por: Capone, Cristiano, et al.
Publicado: (2024)
por: Capone, Cristiano, et al.
Publicado: (2024)
EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems
por: Chen, Jianhong, et al.
Publicado: (2025)
por: Chen, Jianhong, et al.
Publicado: (2025)
Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes
por: Xu, Jian, et al.
Publicado: (2023)
por: Xu, Jian, et al.
Publicado: (2023)
Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning
por: Tebbe, Jörn, et al.
Publicado: (2024)
por: Tebbe, Jörn, et al.
Publicado: (2024)
Constrained Gaussian Process Motion Planning via Stein Variational Newton Inference
por: Li, Jiayun, et al.
Publicado: (2025)
por: Li, Jiayun, et al.
Publicado: (2025)
From Shallow Bayesian Neural Networks to Gaussian Processes: General Convergence, Identifiability and Scalable Inference
por: de Araújo, Gracielle Antunes, et al.
Publicado: (2026)
por: de Araújo, Gracielle Antunes, et al.
Publicado: (2026)
Partially Observable Gaussian Process Network and Doubly Stochastic Variational Inference
por: Kiroriwal, Saksham, et al.
Publicado: (2025)
por: Kiroriwal, Saksham, et al.
Publicado: (2025)
Two-Stage Learned Decomposition for Scalable Routing on Multigraphs
por: Rydin, Filip, et al.
Publicado: (2026)
por: Rydin, Filip, et al.
Publicado: (2026)
Calibrated Inference for the Conditional Average Treatment Effect in the Few-Placebo Regime via Gaussian Processes
por: Uehara, Eichi
Publicado: (2026)
por: Uehara, Eichi
Publicado: (2026)
Accurate and Scalable Stochastic Gaussian Process Regression via Learnable Coreset-based Variational Inference
por: Ketenci, Mert, et al.
Publicado: (2023)
por: Ketenci, Mert, et al.
Publicado: (2023)
Scalable Bayesian Inference in the Era of Deep Learning: From Gaussian Processes to Deep Neural Networks
por: Antoran, Javier
Publicado: (2024)
por: Antoran, Javier
Publicado: (2024)
Provably Efficient Bayesian Optimization with Unknown Gaussian Process Hyperparameter Estimation
por: Ha, Huong, et al.
Publicado: (2023)
por: Ha, Huong, et al.
Publicado: (2023)
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits
por: Chen, Hao, et al.
Publicado: (2021)
por: Chen, Hao, et al.
Publicado: (2021)
Ejemplares similares
-
Randomly Pivoted Partial Cholesky: Random How?
por: Steinerberger, Stefan
Publicado: (2024) -
The Geometry of the Pivot: A Note on Lazy Pivoted Cholesky and Farthest Point Sampling
por: Shabat, Gil
Publicado: (2026) -
Deep Gaussian Covariance Network with Trajectory Sampling for Data-Efficient Policy Search
por: Bogoclu, Can, et al.
Publicado: (2024) -
A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications
por: Logakannan, Krishna Prasath, et al.
Publicado: (2025) -
Gaussian Process Boosting
por: Sigrist, Fabio
Publicado: (2020)