Kernel Methods

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18 papers in the last four weeks, up 125% on the four weeks before. 0.2% of all new papers.

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Large Dimensional Kernel Ridge Regression: Extending to Product Kernels
May 14, 2026Yang Zhou, Yicheng Li, Yuqian Cheng +1Double Descent Kernel Ridge Regression
Department of Statistics and Data Science Tsinghua University Beijing, 100084, China · Department of Mathematical Science Tsinghua University Beijing, 100084, China
Randomized Atomic Feature Models for Physics-Informed Identification of Dynamic Systems
May 14, 2026Rajiv Singh, Mario Sznaier, Lennart LjungRandom Feature Methods System Identification
∗The MathWorks Inc., 1 Apple Hill Drive, Natick, MA 01760 USA · ∗∗ECE Dept., Northeastern University, Boston, MA 02115, USA · ∗∗∗Div. of Automatic Control, Linköping University, Sweden
Wahkon: A Statistically Principled Deep RKHS Superposition Network
May 13, 2026Yongkai Chen, Wenxuan Zhong, Ping MaReproducing Kernel Hilbert Spaces Kernel Methods
Department of Statistics, Harvard University · Department of Statistics, University of Georgia
Support-Conditioned Flow Matching Is Kernel Smoothing
May 13, 2026Daniel Matsui SmolaFlow Matching Cross-Attention
Department of Computer Science University of Washington Seattle, WA
Kernel-based guarantees for nonlinear parametric models in Bayesian optimization
May 13, 2026Rafael OliveiraBayesian Optimization Adaptive Sampling
The Commonwealth Scientific and Industrial Research Organisation (CSIRO) Sydney, Australia
Generative Modeling of Approximately Periodic Time Series by a Posterior-Weighted Gaussian Process
May 13, 2026Elias Reich, Saverio Messineo, Stefan HuberTime Series Generation Gaussian Processes
Josef Ressel Centre for Intelligent and Secure Industrial Automation Salzburg University of Applied Sciences, Salzburg, Austria
DriftXpress: Faster Drifting Models via Projected RKHS Fields
May 12, 2026Ali Falahati, Elliot Creager, Gautam Kamath +1Image Generation One-Step Generative Modeling
Cheriton School of Computer Science, University of Waterloo and Vector Institute. · Department of Electrical and Computer Engineering, University of Waterloo and Vector Institute. · A*STAR Centre for Frontier AI Research, Singapore.
On periodic distributed representations using Fourier embeddings
May 11, 2026Jakeb ChouinardFourier Feature Embeddings Kernel Methods
Department of Systems Design Engineering, University of Waterloo · Centre for Theoretical Neuroscience, University of Waterloo
Scalable Gaussian process inference via neural feature maps
May 11, 2026Anthony StephensonGaussian Processes Kernel Methods
School of Mathematics University of Bristol
Bayesian Optimization with Structured Measurements: A Vector-Valued RKHS Framework
May 10, 2026Wenbin Wang, Colin N. JonesKernel Ridge Regression Bayesian Optimization
Automatic Control Laboratory, EPFL
On Uniform Error Bounds for Kernel Regression under Non-Gaussian Noise
May 10, 2026Johannes Teutsch, Oleksii Molodchyk, Marion Leibold +2Kernel Regression Uncertainty Quantification
Chair of Automatic Control Engineering, Department of Computer Engineering, Technical University of Munich, 80333 Munich, Germany · Institute of Control Systems, Hamburg University of Technology, 21073 Hamburg, Germany · Department of Electrical and Computer Engineering, National University of Singapore, 117583 Singapore
Neurally-plausible radial basis kernels using distributed Fourier embeddings
May 8, 2026Jakeb ChouinardFourier Feature Embeddings Kernel Methods
Department of Systems Design Engineering, University of Waterloo · Centre for Theoretical Neuroscience, University of Waterloo
Don't Get Your Kroneckers in a Twist: Gaussian Processes on High-Dimensional Incomplete Grids
May 8, 2026Mads Greisen Højlund, August Smart Lykke-Møller, Henry Moss +1Kernel Methods Gaussian Process Regression
Department of Chemistry Aarhus University · School of Mathematical Sciences Lancaster University
Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
May 8, 2026Houssam Zenati, Arthur GrettonSemiparametric Inference Causal Effect Estimation
1Gatsby Computational Neuroscience Unit, University College London
Characterizing and Correcting Effective Target Shift in Online Learning
May 8, 2026Ziyan Li, Naoki HirataniLabel Shift Kernel Regression
Department of Physics Washington University in St. Louis St. Louis, USA · Department of Neuroscience Washington University in St. Louis St. Louis, USA
When Symbol Names Should Not Matter: A Logistic Theory of Fresh-Symbol Classification
May 8, 2026Wenjie Guan, Jelena BradicTransformer Classification
Department of Statistics and Data Science, Cornell University
Query-efficient model evaluation using cached responses
May 8, 2026Hayden Helm, Ben Johnson, Carey PriebeKernel Methods
Helivan · Jataware · Johns Hopkins University
A Behavioral Framework for Data-Driven Modeling of Nonlinear Systems in Vector-Valued Reproducing Kernel Hilbert Spaces
May 8, 2026Boya Hou, Maxim RaginskyDynamical Systems Nonlinear System Identification
Carl R. Woese Institute for Genomic Biology and the Coordinated Science Laboratory, University of Illinois Urbana-Champaign, Urbana, IL, 61801 USA · Department of Electrical and Computer Engineering and the Coordinated Science Laboratory, University of Illinois Urbana-Champaign, Urbana, IL, 61801 USA
Kernel Selection is Model Selection: A Unified Complexity-Penalized Approach for MMD Two-Sample Tests
May 7, 2026Yijin Ni, Xiaoming HuoModel Selection Two-Sample Testing
H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, GA 30332, USA
From Token Lists to Graph Motifs: Weisfeiler-Lehman Analysis of Sparse Autoencoder Features
May 7, 2026Ruben Fernandez-Boullon, Pablo Magariños-Docampo, Javier Perez-RoblesTransformer Interpretability Sparse Autoencoders
University of Vigo
Gaussian mixture models in Hilbert spaces via kernel methods
May 7, 2026Daniel López-Montero, Antonio Álvarez-López, Marcos MatabuenaKernel Mean Embeddings Clustering
Friedrich-Alexander-Universität Erlangen-Nürnberg · Universidad Autónoma de Madrid · Mohamed bin Zayed University of Artificial Intelligence
Sharper Guarantees for Misspecified Kernelized Bandit Optimization
May 7, 2026Davide Maran, Csaba SzepesváriMulti-Armed Bandits Kernel Methods
Politecnico di Milano · University of Alberta
Optimal Confidence Band for Kernel Gradient Flow Estimator
May 7, 2026Yuqian Cheng, Zhuo Chen, Qian LinKernel Regression Kernel Methods
Department of Mathematical Sciences Tsinghua University Beijing, 100084, China · Department of Statistics and Data Science2026 Tsinghua University Beijing, 100084, ChinaMay
Convex-Geometric Error Bounds for Positive-Weight Kernel Quadrature
May 7, 2026Satoshi HayakawaConstrained Optimization Kernel Methods
The University of Tokyo
Permutation-preserving Functions and Neural Vecchia Covariance Kernels
May 6, 2026Jian Cao, Nian Liu, Ying LinGaussian Processes Kernel Methods
Department of Mathematics University of Houston Houston, TX 77047 · Department of Industrial and Systems Engineering University of Houston Houston, TX 77047
Transformed Latent Variable Multi-Output Gaussian Processes
May 6, 2026Xiaoyu Jiang, Xinxing Shi, Sokratia Georgaka +2Latent Variable Models Gaussian Processes
Department of Computer Science, University of Manchester, Manchester, UK · Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
A Closed-Form Adaptive-Landmark Kernel for Certified Point-Cloud and Graph Classification
May 5, 2026Sushovan Majhi, Atish Mitra, Žiga Virk +1Point Cloud Classification Classification
Data Science, George Washington University, USA · Department of Mathematical Sciences, Montana Technological University, USA · Faculty of Computer and Information Science, University of Ljubljana, Slovenia +1
Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs
May 5, 2026Eszter Varga-Umbrich, Shikha Surana, Paul Duckworth +3Machine Learning Interatomic Potentials Active Learning
1InstaDeep · University of Cambridge
A Universal Reproducing Kernel Hilbert Space from Polynomial Alignment and IMQ Distance
May 5, 2026Taha BouhsineReproducing Kernel Hilbert Spaces Kernel Methods
Azetta AI
Differentiable Kernel Ridge Regression for Deep Learning Pipelines
May 4, 2026Jean-Marc Mercier, Gabriele SantinKernel Ridge Regression Kernel Regression
1MPG-Partners, Paris, France · Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice, Italy

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Large Dimensional Kernel Ridge Regression: Extending to Product Kernels

Yang Zhou, Yicheng Li, Yuqian Cheng, Qian Lin

Department of Statistics and Data Science Tsinghua University Beijing, 100084, China · Department of Mathematical Science Tsinghua University Beijing, 100084, China

Double Descent Kernel Ridge Regression Kernel Regression Kernel Methods Ridge Regression Generalization Bounds

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Recent studies have reported

\textit{saturation effects}

and

\textit{multiple descent behavior}

in large dimensional kernel ridge regression (KRR). However, these findings are predominantly derived under restrictive settings, such as inner product kernels on sphere or strong eigenfunction assumptions like hypercontractivity. Whether such behaviors hold for other kernels remains an open question. In this paper, we establish a broad, new family of large dimensional kernels and derive the corresponding convergence rates of the generalization error. As a result, we recover key phenomena previously associated with inner product kernels on sphere, including:

i)

the

\textit{minimax optimality}

when the source condition

s\le 1

;

ii)

the

\textit{saturation effect}

when

s>1

;

iii)

\textit{periodic plateau phenomenon}

in the convergence rate and a

\textit {multiple-descent behavior}

with respect to the sample size

n

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Latest papers 168