Calibrated Computation-Aware Gaussian Processes
Fuente:
arXiv
Guardado en:
| Autores principales: | Hegde, Disha, Adil, Mohamed, Cockayne, Jon |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Randomised Postiterations for Calibrated BayesCG
por: Vyas, Niall, et al.
Publicado: (2025)
por: Vyas, Niall, et al.
Publicado: (2025)
Learning to Solve Related Linear Systems
por: Hegde, Disha, et al.
Publicado: (2025)
por: Hegde, Disha, et al.
Publicado: (2025)
Affine Tracing: A New Paradigm for Probabilistic Linear Solvers
por: Hegde, Disha, et al.
Publicado: (2026)
por: Hegde, Disha, et al.
Publicado: (2026)
Computation-Aware Kalman Filtering and Smoothing
por: Pförtner, Marvin, et al.
Publicado: (2024)
por: Pförtner, Marvin, et al.
Publicado: (2024)
An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms
por: Ghandwani, Disha, et al.
Publicado: (2024)
por: Ghandwani, Disha, et al.
Publicado: (2024)
Gaussian Process Regression under Computational and Epistemic Misspecification
por: Sanz-Alonso, Daniel, et al.
Publicado: (2023)
por: Sanz-Alonso, Daniel, et al.
Publicado: (2023)
Constructing Gaussian Processes via Samplets
por: Neugebauer, Marcel
Publicado: (2024)
por: Neugebauer, Marcel
Publicado: (2024)
Bayesian Quadrature: Gaussian Processes for Integration
por: Mahsereci, Maren, et al.
Publicado: (2026)
por: Mahsereci, Maren, et al.
Publicado: (2026)
Preconditioned Additive Gaussian Processes with Fourier Acceleration
por: Wagner, Theresa, et al.
Publicado: (2025)
por: Wagner, Theresa, et al.
Publicado: (2025)
Sketching the Heat Kernel: Using Gaussian Processes to Embed Data
por: Gilbert, Anna C., et al.
Publicado: (2024)
por: Gilbert, Anna C., et al.
Publicado: (2024)
Mode-Shape Expansion Using Physics-Constrained Gaussian Process Regression
por: Ghahari, Farid
Publicado: (2026)
por: Ghahari, Farid
Publicado: (2026)
Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers
por: Pförtner, Marvin, et al.
Publicado: (2022)
por: Pförtner, Marvin, et al.
Publicado: (2022)
Low-rank computation of the posterior mean in Multi-Output Gaussian Processes
por: Esche, Sebastian, et al.
Publicado: (2025)
por: Esche, Sebastian, et al.
Publicado: (2025)
Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration
por: Kim, Hwanwoo, et al.
Publicado: (2024)
por: Kim, Hwanwoo, et al.
Publicado: (2024)
A Gaussian Process Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
por: Mora, Carlos, et al.
Publicado: (2024)
por: Mora, Carlos, et al.
Publicado: (2024)
Posterior Covariance Structures in Gaussian Processes
por: Cai, Difeng, et al.
Publicado: (2024)
por: Cai, Difeng, et al.
Publicado: (2024)
Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential Equations
por: Li, Zhaohui, et al.
Publicado: (2022)
por: Li, Zhaohui, et al.
Publicado: (2022)
Slicing the Gaussian Mixture Wasserstein Distance
por: Piening, Moritz, et al.
Publicado: (2025)
por: Piening, Moritz, et al.
Publicado: (2025)
Gaussian Processes and Reproducing Kernels: Connections and Equivalences
por: Kanagawa, Motonobu, et al.
Publicado: (2025)
por: Kanagawa, Motonobu, et al.
Publicado: (2025)
Revisiting Orbital Minimization Method for Neural Operator Decomposition
por: Ryu, J. Jon, et al.
Publicado: (2025)
por: Ryu, J. Jon, et al.
Publicado: (2025)
The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures
por: Adcock, Ben, et al.
Publicado: (2024)
por: Adcock, Ben, et al.
Publicado: (2024)
Sparse discovery of differential equations based on multi-fidelity Gaussian process
por: Meng, Yuhuang, et al.
Publicado: (2024)
por: Meng, Yuhuang, et al.
Publicado: (2024)
Physics-Informed Geometry-Aware Neural Operator
por: Zhong, Weiheng, et al.
Publicado: (2024)
por: Zhong, Weiheng, et al.
Publicado: (2024)
Large Data Limits of Laplace Learning for Gaussian Measure Data in Infinite Dimensions
por: Zhong, Zhengang, et al.
Publicado: (2026)
por: Zhong, Zhengang, et al.
Publicado: (2026)
Solving All Regression Models For Learning Gaussian Networks Using Givens Rotations
por: Alipourfard, Borzou, et al.
Publicado: (2019)
por: Alipourfard, Borzou, et al.
Publicado: (2019)
Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance
por: Chen, Yifan, et al.
Publicado: (2023)
por: Chen, Yifan, et al.
Publicado: (2023)
Stable Derivative Free Gaussian Mixture Variational Inference for Bayesian Inverse Problems
por: Che, Baojun, et al.
Publicado: (2025)
por: Che, Baojun, et al.
Publicado: (2025)
Flexible and Efficient Probabilistic PDE Solvers through Gaussian Markov Random Fields
por: Weiland, Tim, et al.
Publicado: (2025)
por: Weiland, Tim, et al.
Publicado: (2025)
Exact Gaussian Moment Matching for Residual Networks: a Second-Order Method
por: Kuang, Simon, et al.
Publicado: (2026)
por: Kuang, Simon, et al.
Publicado: (2026)
Wasserstein Bounds for generative diffusion models with Gaussian tail targets
por: Wang, Xixian, et al.
Publicado: (2024)
por: Wang, Xixian, et al.
Publicado: (2024)
Data-driven Learning of Interaction Laws in Multispecies Particle Systems with Gaussian Processes: Convergence Theory and Applications
por: Feng, Jinchao, et al.
Publicado: (2025)
por: Feng, Jinchao, et al.
Publicado: (2025)
A Computationally Efficient Multidimensional Vision Transformer
por: Ichi, Alaa El, et al.
Publicado: (2026)
por: Ichi, Alaa El, et al.
Publicado: (2026)
Decentralized Neural Networks for Robust and Scalable Eigenvalue Computation
por: Katende, Ronald
Publicado: (2024)
por: Katende, Ronald
Publicado: (2024)
From Simple to Complex: Curriculum-Guided Physics-Informed Neural Networks via Gaussian Mixture Models
por: Yang, Jianan, et al.
Publicado: (2026)
por: Yang, Jianan, et al.
Publicado: (2026)
Hybrid Iterative Solvers with Geometry-Aware Neural Preconditioners for Parametric PDEs
por: Lee, Youngkyu, et al.
Publicado: (2025)
por: Lee, Youngkyu, et al.
Publicado: (2025)
Genetic Column Generation for Computing Lower Bounds for Adversarial Classification
por: Penka, Maximilian
Publicado: (2024)
por: Penka, Maximilian
Publicado: (2024)
Fourier Neural Operators for Non-Markovian Processes:Approximation Theorems and Experiments
por: Lee, Wonjae, et al.
Publicado: (2025)
por: Lee, Wonjae, et al.
Publicado: (2025)
Operator SVD with Neural Networks via Nested Low-Rank Approximation
por: Ryu, J. Jon, et al.
Publicado: (2024)
por: Ryu, J. Jon, et al.
Publicado: (2024)
Improving the Predictability of the Madden-Julian Oscillation at Subseasonal Scales with Gaussian Process Models
por: Chen, Haoyuan, et al.
Publicado: (2025)
por: Chen, Haoyuan, et al.
Publicado: (2025)
Computational Advantages of Multi-Grade Deep Learning: Convergence Analysis and Performance Insights
por: Fang, Ronglong, et al.
Publicado: (2025)
por: Fang, Ronglong, et al.
Publicado: (2025)
Ejemplares similares
-
Randomised Postiterations for Calibrated BayesCG
por: Vyas, Niall, et al.
Publicado: (2025) -
Learning to Solve Related Linear Systems
por: Hegde, Disha, et al.
Publicado: (2025) -
Affine Tracing: A New Paradigm for Probabilistic Linear Solvers
por: Hegde, Disha, et al.
Publicado: (2026) -
Computation-Aware Kalman Filtering and Smoothing
por: Pförtner, Marvin, et al.
Publicado: (2024) -
An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms
por: Ghandwani, Disha, et al.
Publicado: (2024)