Cover Image

Machine learning-based system reliability analysis with Gaussian Process Regression

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhou, Lisang, Luo, Ziqian, Pan, Xueting
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Probability
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Obstacle-aware Gaussian Process Regression
by: Shrivastava, Gaurav
Published: (2024)

Algorithms and Scientific Software for Quasi-Monte Carlo, Fast Gaussian Process Regression, and Scientific Machine Learning
by: Sorokin, Aleksei G.
Published: (2025)

Navigating the Landscape of Distributed File Systems: Architectures, Implementations, and Considerations
by: Pan, Xueting, et al.
Published: (2024)

A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes
by: Noack, Marcus M., et al.
Published: (2023)

Gaussian Approximation for Asynchronous Q-learning
by: Rubtsov, Artemy, et al.
Published: (2026)

A Gaussian Comparison Theorem for Training Dynamics in Machine Learning
by: Panahi, Ashkan
Published: (2026)

Random ReLU Neural Networks as Non-Gaussian Processes
by: Parhi, Rahul, et al.
Published: (2024)

Hybrid Probabilistic Forecasting of Under-Five Malaria Admissions in Ghana: A Gaussian Process Regression with Holt-Winters Smoothing
by: Ansah-Narh, T., et al.
Published: (2026)

Robustness quantification: a new method for assessing the reliability of the predictions of a classifier
by: Detavernier, Adrián, et al.
Published: (2025)

Normalizing Flows for Conformal Regression
by: Colombo, Nicolo
Published: (2024)

Test Set Sizing for the Ridge Regression
by: Dubbs, Alexander
Published: (2025)

Efficient Two-Stage Gaussian Process Regression Via Automatic Kernel Search and Subsampling
by: Zhao, Shifan, et al.
Published: (2024)

Gaussian Processes with Sample Paths in Reproducing Kernel Banach Spaces
by: Karvonen, Toni, et al.
Published: (2026)

Sample Path Regularity of Gaussian Processes from the Covariance Kernel
by: Da Costa, Nathaël, et al.
Published: (2023)

Equivariant score-based generative models provably learn distributions with symmetries efficiently
by: Chen, Ziyu, et al.
Published: (2024)

Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions
by: Tran, Hoang, et al.
Published: (2025)

Optimal Transport-Based Domain Adaptation for Rotated Linear Regression
by: Britos, Brian, et al.
Published: (2025)

Bayesian Optimization through Gaussian Cox Process Models for Spatio-temporal Data
by: Mei, Yongsheng, et al.
Published: (2024)

Asymptotic Gaussian Fluctuations of Eigenvectors in Spectral Clustering
by: Lebeau, Hugo, et al.
Published: (2024)

Gaussian Processes and Reproducing Kernels: Connections and Equivalences
by: Kanagawa, Motonobu, et al.
Published: (2025)

Mixing of the No-U-Turn Sampler and the Geometry of Gaussian Concentration
by: Bou-Rabee, Nawaf, et al.
Published: (2024)

Sparsifying Suprema of Gaussian Processes
by: De, Anindya, et al.
Published: (2024)

gp2Scale: A Class of Compactly-Supported Non-Stationary Kernels and Distributed Computing for Exact Gaussian Processes on 10 Million Data Points
by: Noack, Marcus M., et al.
Published: (2025)

Large-width functional asymptotics for deep Gaussian neural networks
by: Bracale, Daniele, et al.
Published: (2021)

Probabilities-Informed Machine Learning
by: Rashki, Mohsen
Published: (2024)

Diffusion Processes on Implicit Manifolds
by: Kawasaki-Borruat, Victor, et al.
Published: (2026)

Rethinking Nonlinearity: Trainable Gaussian Mixture Modules for Modern Neural Architectures
by: Lu, Weiguo, et al.
Published: (2025)

Differentiable Expectation-Maximisation and Applications to Gaussian Mixture Model Optimal Transport
by: Boïté, Samuel, et al.
Published: (2025)

Gaussian Equivalence for Self-Attention: Asymptotic Spectral Analysis of Attention Matrix
by: Hayase, Tomohiro, et al.
Published: (2025)

Quantitative convergence of trained single layer neural networks to Gaussian processes
by: Mosig, Eloy, et al.
Published: (2025)

Multivariate Gaussian Approximation for Random Forest via Region-based Stabilization
by: Shi, Zhaoyang, et al.
Published: (2024)

Regret of exploratory policy improvement and $q$-learning
by: Tang, Wenpin, et al.
Published: (2024)

Recent Advances in Optimal Transport for Machine Learning
by: Montesuma, Eduardo Fernandes, et al.
Published: (2023)

Steady-State Behavior of Constant-Stepsize Stochastic Approximation: Gaussian Approximation and Tail Bounds
by: Wang, Zedong, et al.
Published: (2026)

Machine learning approach to detect dynamical states from recurrence measures
by: Thakur, Dheeraja, et al.
Published: (2024)

Score matching for bridges without learning time-reversals
by: Baker, Elizabeth L., et al.
Published: (2024)

Approximating G(t)/GI/1 queues with deep learning
by: Sherzer, Eliran, et al.
Published: (2024)

Scalable Machine Learning Algorithms using Path Signatures
by: Tóth, Csaba
Published: (2025)

Neural Diffusion Intensity Models for Point Process Data
by: Du, Xinlong, et al.
Published: (2026)

A Dynamics-Informed Gaussian Process Framework for 2D Stochastic Navier-Stokes via Quasi-Gaussianity
by: Hamzi, Boumediene, et al.
Published: (2025)