Image de couverture de livre

High-Dimensional Gaussian Process Regression with Soft Kernel Interpolation

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Camaño, Chris, Huang, Daniel
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Scaling Gaussian Process Regression with Full Derivative Observations
par: Huang, Daniel
Publié: (2025)

Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality
par: Medvedev, Marko, et autres
Publié: (2024)

Optimal Kernel Learning for Gaussian Process Models with High-Dimensional Input
par: Kang, Lulu, et autres
Publié: (2025)

Gaussian Process Regression with Soft Inequality and Monotonicity Constraints
par: Kochan, Didem, et autres
Publié: (2024)

Large Dimensional Kernel Ridge Regression: Extending to Product Kernels
par: Zhou, Yang, et autres
Publié: (2026)

Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression
par: Zou, Lu, et autres
Publié: (2024)

Transformer Neural Processes - Kernel Regression
par: Jenson, Daniel, et autres
Publié: (2024)

Enhancing Predictive Accuracy in Pharmaceutical Sales Through An Ensemble Kernel Gaussian Process Regression Approach
par: Mirshekari, Shahin, et autres
Publié: (2024)

Out-of-Distribution Detection through Soft Clustering with Non-Negative Kernel Regression
par: Gulati, Aryan, et autres
Publié: (2024)

The Theory and Practice of Highly Scalable Gaussian Process Regression with Nearest Neighbours
par: Allison, Robert, et autres
Publié: (2026)

Spectral Anatomy of Quantum Gaussian Process Kernels
par: Xu, Jian, et autres
Publié: (2026)

Scalable Deep Basis Kernel Gaussian Processes
par: Zhu, Yunqin, et autres
Publié: (2025)

Likelihood-Free Gaussian Process for Regression
par: Shikuri, Yuta
Publié: (2020)

Implicit Manifold Gaussian Process Regression
par: Fichera, Bernardo, et autres
Publié: (2023)

Robust and Conjugate Gaussian Process Regression
par: Altamirano, Matias, et autres
Publié: (2023)

On Uniform Error Bounds for Kernel Regression under Non-Gaussian Noise
par: Teutsch, Johannes, et autres
Publié: (2026)

Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning
par: He, Fan, et autres
Publié: (2024)

Obstacle-aware Gaussian Process Regression
par: Shrivastava, Gaurav
Publié: (2024)

An Intuitive Tutorial to Gaussian Process Regression
par: Wang, Jie
Publié: (2020)

Expressive Mortality Models through Gaussian Process Kernels
par: Ludkovski, Mike, et autres
Publié: (2023)

Gaussian Interpolation Flows
par: Gao, Yuan, et autres
Publié: (2023)

Relative Information Gain and Gaussian Process Regression
par: Flynn, Hamish
Publié: (2025)

Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regression
par: Ashtiani, Shaghayegh Z., et autres
Publié: (2024)

Sparse Attention as Compact Kernel Regression
par: Santos, Saul, et autres
Publié: (2026)

Gaussian Process Regression under Computational and Epistemic Misspecification
par: Sanz-Alonso, Daniel, et autres
Publié: (2023)

Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization
par: Xu, Zhitong, et autres
Publié: (2024)

Privacy-aware Gaussian Process Regression
par: Tuo, Rui, et autres
Publié: (2023)

SEEK: Self-adaptive Explainable Kernel For Nonstationary Gaussian Processes
par: Negarandeh, Nima, et autres
Publié: (2025)

Efficient Two-Stage Gaussian Process Regression Via Automatic Kernel Search and Subsampling
par: Zhao, Shifan, et autres
Publié: (2024)

Sparse Techniques for Regression in Deep Gaussian Processes
par: Latz, Jonas, et autres
Publié: (2025)

Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression
par: Cheng, Lixue, et autres
Publié: (2022)

Distributionally Robust Active Learning for Gaussian Process Regression
par: Takeno, Shion, et autres
Publié: (2025)

Guaranteed Coverage Prediction Intervals with Gaussian Process Regression
par: Papadopoulos, Harris
Publié: (2023)

High-Dimensional Tail Index Regression
par: Sasaki, Yuya, et autres
Publié: (2024)

On LASSO for High Dimensional Predictive Regression
par: Mei, Ziwei, et autres
Publié: (2022)

Generalization in Kernel Regression Under Realistic Assumptions
par: Barzilai, Daniel, et autres
Publié: (2023)

Spectrally Transformed Kernel Regression
par: Zhai, Runtian, et autres
Publié: (2024)

Revisiting Kernel Attention with Correlated Gaussian Process Representation
par: Bui, Long Minh, et autres
Publié: (2025)

The Price of Linear Time: Error Analysis of Structured Kernel Interpolation
par: Moreno, Alexander, et autres
Publié: (2025)

Training-Free Generative Modeling via Kernelized Stochastic Interpolants
par: Coeurdoux, Florentin, et autres
Publié: (2026)