Cover Image

Permutation-preserving Functions and Neural Vecchia Covariance Kernels

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cao, Jian, Liu, Nian, Lin, Ying
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Computation
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Neural-Mean Vecchia Gaussian Process for Unified Argo Modeling
by: Liu, Nian, et al.
Published: (2025)

Implementation and Analysis of GPU Algorithms for Vecchia Approximation
by: James, Zachary, et al.
Published: (2024)

Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks
by: Jimenez, Felix, et al.
Published: (2023)

Taming Extreme Tokens: Covariance-Aware GRPO with Gaussian-Kernel Advantage Reweighting
by: Wang, Cheng, et al.
Published: (2026)

The Generalised Kernel Covariance Measure
by: Bergen, Luca, et al.
Published: (2026)

Learnable Permutation for Structured Sparsity on Transformer Models
by: Li, Zekai, et al.
Published: (2026)

Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models
by: Kündig, Pascal, et al.
Published: (2023)

Linear-Cost Vecchia Approximation of Multivariate Normal Probabilities
by: Cao, Jian, et al.
Published: (2023)

Learning a Depth Covariance Function
by: Dexheimer, Eric, et al.
Published: (2023)

Neural-Symbolic Recursive Machine for Systematic Generalization
by: Li, Qing, et al.
Published: (2022)

Learning Permutation-invariant Macroscopic Dynamics
by: Han, Zhichao, et al.
Published: (2026)

PEARL: Towards Permutation-Resilient LLMs
by: Chen, Liang, et al.
Published: (2025)

Self-Supervised Learning for Neural Topic Models with Variance-Invariance-Covariance Regularization
by: Xu, Weiran, et al.
Published: (2025)

Deep Incomplete Multi-view Learning via Cyclic Permutation of VAEs
by: Gao, Xin, et al.
Published: (2025)

Linear Transformers with Learnable Kernel Functions are Better In-Context Models
by: Aksenov, Yaroslav, et al.
Published: (2024)

Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models
by: Tang, Raphael, et al.
Published: (2023)

A Permutation-free Kernel Two-Sample Test
by: Shekhar, Shubhanshu, et al.
Published: (2022)

Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics
by: Liu, Xin-Yang, et al.
Published: (2022)

Understanding Matrix Function Normalizations in Covariance Pooling through the Lens of Riemannian Geometry
by: Chen, Ziheng, et al.
Published: (2024)

Kernel-Smith: A Unified Recipe for Evolutionary Kernel Optimization
by: Du, He, et al.
Published: (2026)

Computational Efficiency under Covariate Shift in Kernel Ridge Regression
by: Della Vecchia, Andrea, et al.
Published: (2025)

LLM as Graph Kernel: Rethinking Message Passing on Text-Rich Graphs
by: Zhang, Ying, et al.
Published: (2026)

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization
by: Chen, Yihang, et al.
Published: (2024)

Divergence is Uncertainty: A Closed-Form Posterior Covariance for Flow Matching
by: Xing, Jiarui, et al.
Published: (2026)

Better Prompt Compression Without Multi-Layer Perceptrons
by: Honig, Edouardo, et al.
Published: (2025)

Fairness via Independence: A (Conditional) Distance Covariance Framework
by: Huang, Ruifan, et al.
Published: (2024)

On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains
by: Li, Yicheng, et al.
Published: (2023)

Biologically Plausible Training of Deep Neural Networks Using a Top-down Credit Assignment Network
by: Chen, Jian-Hui, et al.
Published: (2022)

Differentially Private Permutation Tests: Applications to Kernel Methods
by: Kim, Ilmun, et al.
Published: (2023)

Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation
by: Yan, Bo, et al.
Published: (2023)

A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models
by: Gruber, Sebastian G., et al.
Published: (2023)

Community Detection with Heterogeneous Block Covariance Model
by: Li, Xiang, et al.
Published: (2024)

Towards Automated Kernel Generation in the Era of LLMs
by: Yu, Yang, et al.
Published: (2026)

Multivariate Long-term Time Series Forecasting with Fourier Neural Filter
by: Xu, Chenheng, et al.
Published: (2025)

A Scalable Nystrom-Based Kernel Two-Sample Test with Permutations
by: Chatalic, Antoine, et al.
Published: (2025)

Covariance Density Neural Networks
by: Roy, Om, et al.
Published: (2025)

Spatiotemporal Covariance Neural Networks
by: Cavallo, Andrea, et al.
Published: (2024)

Sparse Covariance Neural Networks
by: Cavallo, Andrea, et al.
Published: (2024)

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selections and Approximations
by: Fang, Kun, et al.
Published: (2025)

The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs
by: Mostowsky, Peter, et al.
Published: (2024)