Kernel Method

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Kernel Method

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Kernel Hilbert Spaces

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Kernel Ridge Regression

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AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks
May 31, 2026Zhongyue Zhang, Guangyin Jin, Yuxuan Liang +2Graph Neural Networks Kernel Method
On Finite-sample Concentration of Median of Incomplete U-Statistics
May 30, 2026Nong Minh Hieu, Antoine LedentGeneralized U-Statistics Estimators
Perturbative methods for non-parametric instrumental variable
May 29, 2026Wei Bu, Arthur GrettonKernel Ridge Regression Perturbation Propagation
Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach
May 29, 2026Baptiste Leroux, Clément Dombry, Anne SabourinQuantile Regression Support Vector Machines
Ridge Regression from Poisson Resetting: A Renewal Perspective on Spectral Regularization
May 28, 2026Petar JolakoskiKernel Ridge Regression Covariance Estimation
Eigen-Spike Emergence and Quadratic Equivalents for Conjugate Kernels on Nonlinearly Separable Data
May 28, 2026Collin Cranston, Zhichao Wang, Todd Kemp +1Kernel Method Separability
FreeForm: Reduced-Order Deformable Simulation from Particle-Based Skinning Eigenmodes
May 28, 2026Donglai Xiang, Vismay Modi, Rishit Dagli +5Finite Element Method Reduced-Order Models
Optimal ridge regularization revisited
May 27, 2026Jack Timmermans, Sergio A. AlvarezKernel Ridge Regression Regularization Techniques
Probabilistic Smoothing with Ratio-Monotone Transforms for Global Optimization
May 26, 2026Kukyoung Jang, Taehyun Cho, Junrui Zhang +2Smoothing Kernel Method
Nonlinear Data Integration via Kernel Methods for Data Collaboration Analysis
May 26, 2026Yamato Suetake, Yuta Kawakami, Shunnosuke Ikeda +1Kernel Method Dimensionality Reduction
SPHERE-JEPA: Spherical Prediction with Homogeneous Embeddings
May 26, 2026Léo Nicollier, Max Dunitz, Marc Pic +3Manifold Learning Method Embedding Dimension
Near-Optimal Regret in Adversarial Kernel Bandits
May 26, 2026Yu-Jie Zhang, Hao Qiu, Jonathan Scarlett +1Kernel Hilbert Spaces Regret
SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learning
May 26, 2026Wenyuan Zhao, Rui Tuo, Chao TianGaussian Process Multi-Gpu Systems
Conditional KRR: Injecting Unpenalized Features into Kernel Methods with Applications to Kernel Thresholding
May 25, 2026Rustem Takhanov, Zhenisbek AssylbekovKernel Ridge Regression Kernel Method
Branched Signature Kernel Solvers for ODEs with rough Single-Trajectory signals
May 25, 2026Munawar Ali, Qi Feng, Charlie Pyle +1Ordinary Differential Equations Kernel Method
Efficient Benchmarking Is Just Feature Selection and Multiple Regression
May 25, 2026Sam Bowyer, Acyr Locatelli, Kris CaoBenchmarking Large Language Models Relevance
Learning View-Dependent Splatting Kernels
May 25, 2026Huakeng Ding, Zhanpeng Liu, Fan Pei +2Novel View Synthesis Gaussian Splatting
Data-Specific Hyper-Parameter Design: A Paradigm Shift in Reservoir Computing
May 24, 2026G Manjunath, Juan-Pablo Ortega, Alma van der MerweReservoir Computing Reservoir
Nyström Kernel Stein Discrepancy Tests
May 24, 2026Florian Kalinke, Zoltán Szabó, Bharath K. SriperumbudurStatistical Models Kernel Method
Optimizing Multidimensional Scaling in Gini Metric Spaces
May 24, 2026Cassandra Mussard, Stéphane MussardMaximum Mean Discrepancy Gaussian Mixture Models
LAPLEX: The FFT of Learnable Laplace Kernels
May 23, 2026Łukasz Struski, Hanna Blazhko, Piotr Kubaty +1Matrix Multiplication Kernel Method
Distance-Aware Joint Spatio-Temporal Graph Contrastive Learning for Major Depressive Disorder Diagnosis
May 22, 2026Muhammad Asif Hasan, Yanming Zhu, Xuefei Yin +1Functional Networks Contrastive Learning
Any-Dimensional Invariant Universality
May 22, 2026Shengtai Yao, Eitan Levin, Mateo DíazUniversality Metric Spaces
A Martingale Kernel Independence Test
May 21, 2026Felix Laumann, Zhaolu Liu, Mauricio BarahonaGeneralized U-Statistics Conditional Independence
Adaptive Measurement Allocation for Learning Kernelized SVMs Under Noisy Observations
May 21, 2026Artur MiroszewskiQuantum Kernels Support Vector Machines
IKNO: Infinite-order Kernel Neural Operators
May 21, 2026Pengyuan Zhu, Ivor W. Tsang, Yueming LyuNeural Operators Fourier Neural Operators
Exact Uniform L1 Spacing for Solow-Polasky Diversity on Lines and Ordered Pareto Fronts
May 21, 2026Michael T. M. Emmerich, Mahboubeh Nezhadmoghaddam, Jesús Guillermo Falcón CardonaPareto Frontier Maximum Independent Set
Adaptive RBF-KAN: A Comparative Evaluation of Dynamic Shape Parameters in Kolmogorov-Arnold Networks
May 20, 2026Roberto Cavoretto, Alessandra De Rossi, Adeeba Haider +1Kolmogorov-Arnold Networks Radial Basis Function
Generalized Functional ANOVA in Closed-Form: A Unified View of Additive Explanations
May 18, 2026Baptiste Ferrere, Nicolas Bousquet, Fabrice Gamboa +1Explainability Logistic Regression
3D Skew Gaussian Splatting with Any Camera Trajectory Visualization Engine
May 18, 2026Beizhen Zhao, Yifan Zhou, Gaochao Song +23D Gaussian Rasterization
Automated Kernel Discovery Towards Understanding High-dimensional Bayesian Optimization
May 18, 2026Taeyoung Yun, Woocheol Shin, Inhyuck Song +2Bayesian Optimization Gaussian Process
How does feature learning reshape the function space?
May 18, 2026João Lobo, Bruno Loureiro, Long Tran-Than +1Feature Learning Kernel Method
TabKDE: Simple and Scalable Tabular Data Generation with Kernel Density Estimates
May 17, 2026Meysam Alishahi, Yan Zheng, Junpeng Wang +2Synthetic Tabular Data Gaussian Copula
Controlling False Discovery in Arbitrarily Structured Hypothesis Spaces via Reproducing Kernels
May 17, 2026Binyamin Perets, Shie MannorFalse Discovery Rate Kernel Hilbert Spaces
CompactAttention: Accelerating Chunked Prefill with Block-Union KV Selection
May 16, 2026Jiwon Song, Dongwon Jo, Beomseok Kang +1Prefill Efficient Long-Context Inference
Diversified Residual Symbolic Regression
May 15, 2026Koki Ikeda, Masahiro Nomura, Ryoki HamanoSymbolic Regression Kernel Ridge Regression
Universal Approximation of Nonlinear Operators and Their Derivatives
May 14, 2026Filippo de FeoProbabilistic Operator Learning Logarithmic Sobolev Inequalities
Average Gradient Outer Product in kernel regression provably recovers the central subspace for multi-index models
May 14, 2026Libin Zhu, Damek Davis, Dmitriy Drusvyatskiy +1Kernel Ridge Regression Kernel Method
Distance-Matrix Wasserstein Statistics for Scalable Gromov--Wasserstein Learning
May 14, 2026Ao Xu, Tieru WuGromov--Wasserstein Wasserstein Distance
Large Dimensional Kernel Ridge Regression: Extending to Product Kernels
May 14, 2026Yang Zhou, Yicheng Li, Yuqian Cheng +1Kernel Ridge Regression Kernel Method
On Kernel Eigen-alignments of KRR: Reconstruction and Generalization
May 14, 2026Yang Liu, Ernest Fokoue, Richard Lange +1Kernel Ridge Regression Largest Eigenvalue
Randomized Atomic Feature Models for Physics-Informed Identification of Dynamic Systems
May 14, 2026Rajiv Singh, Mario Sznaier, Lennart LjungSystem Identification Physics-Informed Machine Learning
Wavelet-Based Observables for Koopman Analysis: An Extended Dynamic Mode Decomposition Framework
May 14, 2026Cankat Tilki, Serkan GugercinKoopman Operator Discrete Wavelet Transform
Wahkon: A Statistically Principled Deep RKHS Superposition Network
May 13, 2026Yongkai Chen, Wenxuan Zhong, Ping MaKernel Hilbert Spaces Kolmogorov-Arnold Networks
Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models
May 13, 2026Gregory Beurier, Robin Reiter, Camille Noûs +2Spectra Kernel Ridge Regression
Support-Conditioned Flow Matching Is Kernel Smoothing
May 13, 2026Daniel Matsui SmolaKernel Method Conditional Flow Matching
Constitutional Governance in Metric Spaces
May 13, 2026Ehud Shapiro, Nimrod TalmonDigital Sovereignty Consensus
Chem-GMNet: A Sphere-Native Geometric Transformer for Molecular Property Prediction
May 13, 2026Deepak Warrier, Raja Sekhar PappalaMolecular Structure Elucidation Molecular Property Prediction
Kernel-based guarantees for nonlinear parametric models in Bayesian optimization
May 13, 2026Rafael OliveiraBayesian Optimization Bayesian
State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
May 13, 2026Hoang-Son Tran, Pranav Gupta, Rémi Bardenet +1Mini-Batch Stochastic Gradient Descent Subsampling
FePySR: A Neural Feature Extraction Framework for Efficient and Scalable Symbolic Regression
May 12, 2026Zhiming Yu, Wangtao Lu, Xin LaiSymbolic Regression Kernel Ridge Regression
DriftXpress: Faster Drifting Models via Projected RKHS Fields
May 12, 2026Ali Falahati, Elliot Creager, Gautam Kamath +1Generative Models Image Denoising
Sobolev Regularized MMD Gradient Flow
May 12, 2026Chenyang Tian, Bharath K. Sriperumbudur, Arthur Gretton +1Maximum Mean Discrepancy Wasserstein Gradient Flows
Stop Marginalizing My Dreams: Model Inversion via Laplace Kernel for Continual Learning
May 12, 2026Patryk Krukowski, Jacek Tabor, Przemysław Spurek +2Efficient Continual Learning Continual Learning Methods
Exact Stiefel Optimization for Probabilistic PLS: Closed-Form Updates, Error Bounds, and Calibrated Uncertainty
May 12, 2026Haoran Hu, Xingce WangLeast Squares Calibrated Uncertainty
Interpretable Machine Learning for Spatial Science: A Lie-Algebraic Kernel for Rotationally Anisotropic Gaussian Processes
May 11, 2026Kane Warrior, Dalia ChakrabartyGaussian Process Anisotropy
Kernel-Gradient Drifting Models
May 11, 2026Maria Esteban-Casadevall, Jorge Carrasco-Pollo, Max Welling +3Wasserstein Gradient Flows Generative Models
Multifidelity Gaussian process regression for solving nonlinear partial differential equations
May 11, 2026Fatima-Zahrae El-Boukkouri, Josselin Garnier, Olivier RoustantGaussian Process Classical Numerical Solver
Nearly-Optimal Algorithm for Adversarial Kernelized Bandits
May 11, 2026Shogo IwazakiGeneralized Linear Bandit Bandits
Scalable Gaussian process inference via neural feature maps
May 11, 2026Anthony StephensonGaussian Process Kernel Method

May 31, 2026 · cs.LGJ/K move · Enter open · S save

AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks

Zhongyue Zhang, Guangyin Jin, Yuxuan Liang, Suwan Yin+1

Modeling spatial dependencies is central to spatiotemporal data analysis using Graph Neural Networks (GNNs). Traditional methods rely on distance-based kernels with predefined parameters, which restricts model capacity. Although generic adaptive mechanisms (e.g., Graph Attention Networks) offer flexibility, they often fail to capture the underlying geometric structure, performing worse than distance-based models in data-sparse scenarios. Addressing this, we revisit the kernel parameterization problem and theoretically prove that misspecified kernel parameters introduce unavoidable approximation errors in GNNs. To overcome this, we propose AdaKernel, a simple yet effective approach that learns adaptive kernel parameters within the neural network. Unlike methods that learn graph structures from scratch, AdaKernel adopts a structure-preserving strategy that optimizes the scale of physical interactions rather than discarding them. Extensive experiments on Kriging, Imputation, and Forecasting demonstrate that AdaKernel consistently improves various GNN architectures and outperforms model-agnostic adaptive baselines, validating that accurately learned kernel parameters are superior to both fixed priors and fully latent graph structures.

Graph Neural Networks Kernel Method Temporal Dependence Spatiotemporal

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