Image de couverture de livre

Regular Fourier Features for Nonstationary Gaussian Processes

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Jawaid, Arsalan, Karatas, Abdullah, Seewig, Jörg
Format:	Preprint
Publié:	2026
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

Feature Characterization for Profile Surface Texture
par: Müller, Alexander, et autres
Publié: (2024)

Surface data imputation with stochastic processes
par: Jawaid, Arsalan, et autres
Publié: (2024)

Model‐based analysis of the influence of surface roughness on fatigue processes
par: Sikang Yan, et autres
Publié: (2024)

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

Predicting Covariate-Driven Spatial Deformation for Nonstationary Gaussian Processes
par: Gu, Minghao, et autres
Publié: (2026)

Adaptive RKHS Fourier Features for Compositional Gaussian Process Models
par: Shi, Xinxing, et autres
Publié: (2024)

A Framework for Nonstationary Gaussian Processes with Neural Network Parameters
par: James, Zachary, et autres
Publié: (2025)

Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes
par: Cheema, Talay M, et autres
Publié: (2023)

Nonstationary Sparse Spectral Permanental Process
par: Sun, Zicheng, et autres
Publié: (2024)

Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process
par: Saito, Issei, et autres
Publié: (2025)

Causal Inference from Slowly Varying Nonstationary Processes
par: Du, Kang, et autres
Publié: (2024)

Accurate Remaining Useful Life Prediction with Uncertainty Quantification: a Deep Learning and Nonstationary Gaussian Process Approach
par: Xu, Zhaoyi, et autres
Publié: (2021)

Preconditioned Additive Gaussian Processes with Fourier Acceleration
par: Wagner, Theresa, et autres
Publié: (2025)

Graph Random Features for Scalable Gaussian Processes
par: Zhang, Matthew, et autres
Publié: (2025)

Regularized Multi-output Gaussian Convolution Process with Domain Adaptation
par: Xinming, Wang, et autres
Publié: (2024)

EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems
par: Chen, Jianhong, et autres
Publié: (2025)

Nonstationary Reinforcement Learning with Linear Function Approximation
par: Zhou, Huozhi, et autres
Publié: (2020)

Describing Nonstationary Data Streams in Frequency Domain
par: Komorniczak, Joanna
Publié: (2025)

Graph Classification Gaussian Processes via Hodgelet Spectral Features
par: Alain, Mathieu, et autres
Publié: (2024)

R^2-HGP: A Double-Regularized Gaussian Process for Heterogeneous Transfer Learning
par: Wang, Duo, et autres
Publié: (2025)

Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes
par: Xu, Jian, et autres
Publié: (2023)

Adaptive Linear Embedding for Nonstationary High-Dimensional Optimization
par: Wen, Yuejiang, et autres
Publié: (2025)

A State-Space Approach to Nonstationary Discriminant Analysis
par: Xie, Shuilian, et autres
Publié: (2025)

Online Conformal Model Selection for Nonstationary Time Series
par: Li, Shibo, et autres
Publié: (2025)

Causal Temporal Representation Learning with Nonstationary Sparse Transition
par: Song, Xiangchen, et autres
Publié: (2024)

Sample Path Regularity of Gaussian Processes from the Covariance Kernel
par: Da Costa, Nathaël, et autres
Publié: (2023)

Random Fourier Signature Features
par: Toth, Csaba, et autres
Publié: (2023)

Plastic Learning with Deep Fourier Features
par: Lewandowski, Alex, et autres
Publié: (2024)

Learning to Query History: Nonstationary Classification via Learned Retrieval
par: Gammell, Jimmy, et autres
Publié: (2026)

Nonstationary Generalized Linear Bandits with Discounted Online Mirror Descent
par: Lee, Joongkyu, et autres
Publié: (2026)

Nonstationary Time Series Forecasting via Unknown Distribution Adaptation
par: Li, Zijian, et autres
Publié: (2024)

Fast Rates for Nonstationary Weighted Risk Minimization
par: Brock, Tobias, et autres
Publié: (2026)

Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization
par: Sahu, Sharan, et autres
Publié: (2026)

Identifying Nonstationary Causal Structures with High-Order Markov Switching Models
par: Balsells-Rodas, Carles, et autres
Publié: (2024)

Self-Organizing Recurrent Stochastic Configuration Networks for Nonstationary Data Modelling
par: Dang, Gang, et autres
Publié: (2024)

Fourier Feature Pyramids for Physics-Informed Neural Networks
par: Zhao, Brandon, et autres
Publié: (2026)

Modeling Nonstationary Extremal Dependence via Deep Spatial Deformations
par: Shao, Xuanjie, et autres
Publié: (2025)

On the Provable Suboptimality of Momentum SGD in Nonstationary Stochastic Optimization
par: Sahu, Sharan, et autres
Publié: (2026)

ANOVA-boosting for Random Fourier Features
par: Potts, Daniel, et autres
Publié: (2024)

Locally Stationary Graph Processes
par: Canbolat, Abdullah, et autres
Publié: (2023)