Kernel Methods

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Overview

Kernel methods represent data through similarity functions, allowing linear algorithms in feature spaces to model nonlinear patterns without explicitly constructing those spaces. Common approaches include support vector machines and kernel ridge regression for prediction, Gaussian processes for uncertainty, and kernel mean embeddings and maximum mean discrepancy for comparing distributions and testing dependence. Researchers also use kernels to learn dynamical systems and operators, and scale computations with random features, Nyström approximations, and structured kernels. A central tension is balancing expressive, well-chosen kernels and reliable guarantees against the computation and sensitivity to kernel or bandwidth selection that can limit practical use.

Latest papers

CardsList

234 papers40 papers in the last four weeks, up 111% on the four weeks before. 0.2% of all new papers.

  1. CFKS: Continuum-Feature Kernel PDE Solvers

    Oct 28, 2026Vikas Dwivedi, Balaji Srinivasan, Monica Sigovan +1PDE SolvingKernel Methods

    CREATIS Biomedical Imaging Laboratory INSA, CNRS UMR 5220, Inserm, Université Lyon 1 Lyon 69621, France · Wadhwani School of Data Science and AI IIT Madras, India

  2. Anchored Stein Variational Gradient Descent Algorithm for Non-smooth Sampling

    Oct 26, 2026Thanh Dang, Matthew X. Burns, Jiaming LiangStein Variational Gradient DescentSampling from Unnormalized Distributions

    Department of Computer Science, University of Rochester, Rochester, NY 14620. · Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627 · Goergen Institute for Data Science and Artificial Intelligence and Department of Computer Science, University of Rochester, Rochester, NY 14620

  3. Joint Independence Testing for Latent Position Random Graphs

    Oct 25, 2026Xiaoyi Wen, Zoltan SzaboKernel Methods

    Department of Epidemiology and Biostatistics University of California, San Francisco San Francisco, USA · Department of Statistics London School of Economics London, UK

  4. Finite-Sample Sensitivity of Kernel Metrics for Generative Evaluation

    Oct 25, 2026Tomasz Radzikowski, Mateusz ModrzejewskiGenerative AI EvaluationKernel Methods

    Institute of Computer Science Warsaw University of Technology Warsaw, Poland

  5. The Clustering Occupation Kernel (CLOCK) Method for Time Series Arising from Dynamical Systems

    Oct 23, 2026Michael L. Wells, Kamel Lahouel, Victor Rielly +1Time Series ClusteringKernel Methods

    Fariborz Maseeh Department of Mathematics and Statistics, Portland State University, Portland, OR 97201 · Translational Genomics Research Institute, Phoenix, AZ 85004

  6. Kernelised covariance matrices, dimension reduction and imbalanced spectral clustering

    Oct 23, 2026Xiaoyu Dong, Subhro Ghosh, Soumendu Sundar Mukherjee +1ClusteringSpectral Clustering

    National University of Singapore, Singapore · Indian Statistical Institute, Kolkata, India

  7. Flow-based Spectral Kernel Learning for Nonstationary Attention

    Oct 21, 2026Zi Yang, Ying Li, Alejandro Lancho +3Kernel MethodsEfficient Attention

    School of Computing and Data Science, The University of Hong Kong · Department of Statistics and Data Science, National University of Singapore · Department of Signal Theory and Communications, Universidad Carlos III de Madrid

  8. Local and Sparse Kernel Operator Learning for Incompressible Flows: An Operator Lagrange Formulation

    Oct 21, 2026Ramansh Sharma, Matthew Lowery, Houman Owhadi +1PDE Operator LearningKernel Methods

    Kahlert School of Computing, University of Utah, UT, USA · Department of Computing and Mathematical Sciences, California Institute of Technology, CA, USA

  9. Gaussian Approximation of Besov Functions for Logistic Classification on Manifolds

    Oct 19, 2026Guanhang Lei, Lei Shi, Xiaoming YuanClassificationStatistical Learning Theory

    Department of Mathematics, The University of Hong Kong, Hong Kong, China · School of Mathematical Sciences, Shanghai Key Laboratory for Contemporary Applied Mathematics, Center for Applied Mathematics, Fudan University, Shanghai, China

  10. R2-GP: Variance-Calibrated Priors for Additive Gaussian Processes

    Oct 19, 2026Javier Enrique Aguilar, Soham Mukherjee, David KohnsGaussian ProcessesBayesian Inference

    TU Dortmund, RC TRUST (UA Ruhr), Germany · Aalto University, Finland

  11. Optimal Multi-Kernel Combination for Online Multi-Label Classification

    Oct 19, 2026Tingting Zhai, Wei Liu, Renjian YuMulti-Label ClassificationKernel Methods

    College of Information and Artificial Intelligence, Yangzhou University, Yangzhou, China, 225127

  12. Structured Relevance Determination and Channel Additive Gaussian Processes for Image Data

    Oct 18, 2026Harald Roßels, Ludovico ScartonGaussian ProcessesKernel Methods

    Independent Researcher · Bonn-Rhein-Sieg University of Applied Sciences

  13. Scalable and Adaptive Distribution Regression via Random Features

    Oct 17, 2026Yinsong Wang, Xiao Liu, Yu DingDistributional RegressionRandom Feature Methods

    School of Mechanical, Industrial and Material Engineering, University of Nevada Reno, Reno 89557, USA. · H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta 30332, USA.

  14. Structure-Aware Spatial Kernels for Gaussian Process Distributed Port-Hamiltonian Systems

    Oct 15, 2026Tianyi Yang, Thomas BeckersKernel MethodsGaussian Processes

    Department of Computer Science, Vanderbilt University, Nashville, TN 37212, USA · Institute for Measurement, Control and Microtechnology, Ulm University, 89081 Ulm, Germany

  15. Phase Kernel Lifts Capacity of Dense Associative Memory

    Oct 15, 2026Yifei Zhao, Ying TangHopfield NetworksAssociative Memory

    Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China · School of Physics, University of Electronic Science and Technology of China, Chengdu 611731, China · Key Laboratory of Quantum Physics and Photonic Quantum Information, Ministry of Education, University of Electronic Science and Technology of China, Chengdu 611731, China +1

  16. SKG: A Law-Induced Engine from Inference to Induction

    Oct 14, 2026Jian An, Chaoshi Wu, Jinhui WuPhysics-Informed MLKernel Methods

  17. Reliable Risk Estimation with Kernel Mean Matching under Dependent Sampling

    Oct 13, 2026Alexey Zaytsev, Diana Koldasbayeva, Egor SerovKernel MethodsStatistical Learning Theory

    Applied AI Institute

  18. Hierarchical Time Series Forecasting with Operator-Valued Kernels

    Oct 13, 2026Junjie Yang, Adeline Fermanian, Claire Boyer +1Time Series ForecastingKernel Methods

    LPSM · LMO, IUF

  19. Learning Incompressible 3D Flows from Sparse Samples: A Set Transformer Approach to Divergence-Free Kernel Fitting

    Oct 12, 2026Ramzi Idoughi, Radouan Boukharfane, Peter WonkaSet TransformerComputational Fluid Dynamics

    King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia · Mohammed VI Polytechnic University (UM6P), Benguerir, Morocco

  20. Random Fourier Features in Near-Linear Time

    Oct 10, 2026Sepideh Mahabadi, Sandeep Silwal, Tal WagnerRandom Feature MethodsFourier Feature Embeddings

    Microsoft Research – Redmond · University of Wisconsin-Madison · Tel Aviv University

  21. Learnable Low-Rank Polynomial Sketch for Effective Linear Attention

    Oct 9, 2026Zongyue Qin, Haoran Deng, Jason Cong +1Transformer AttentionLinear Attention

    Department of Computer Science, University of California Los Angeles, CA, USA

  22. Scalable Learning of Determinantal Point Processes with Kernel-Structured Interactions

    Oct 9, 2026Takahiro Kawashima, Hideitsu HinoDeterminantal Point ProcessKernel Methods

    ZOZO Research · Institute of Statistical Mathematics · Waseda University

  23. Kernelized Parameter Sensitivity Atlases for Amortized Conditional Interpretability

    Oct 8, 2026Davide Wiest, Ahanaf Hasan Ariq, Abdul Monaf ChowdhuryNeural Network InterpretabilityGradient-Based Attribution

    Technische Universität Darmstadt · Daffodil International School · University of Dhaka

  24. Bilevel optimization for data-driven learning of Koopman embeddings using kernel-based autoencoders

    Oct 8, 2026Joel-Pascal Ntwali N'konzi, Feliks Nüske, Stefan KlusExtended Dynamic Mode DecompositionKoopman Operator Learning

    Maxwell Institute for Mathematical Sciences, The University of Edinburgh & Heriot–Watt University, Edinburgh, United Kingdom · Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany · LAAS-CNRS, Université de Toulouse, Toulouse, France +1

  25. Using Weisfeiler-Leman Features for Algorithm Selection in Constraint Optimisation

    Oct 8, 2026Alessio Pellegrino, Jacopo MauroAlgorithm SelectionConstraint Programming

    University of Southern Denmark, Denmark

  26. Conditional Kernel Stein Discrepancy

    Oct 8, 2026Federico Matteucci, Florian KalinkeKernel MethodsStatistical Hypothesis Testing

    Karlsruhe Institute of Technology

  27. Feature Space Adaptation for Effortless Gaussian Process Flows

    Oct 8, 2026Thomas Cowperthwaite, Louis Sharrock, Lachlan Astfalck +1Maximum Marginal Likelihood EstimationGaussian Processes

    University of Cambridge · University College London · University of New South Wales +1

  28. Kernel Autoresearch for Open-Ended Model Discovery

    Oct 7, 2026Richard Cornelius Suwandi, Feng Yin, Kevin MurphyKernel MethodsAutomated Algorithm Discovery

    CUHK-Shenzhen · University of British Columbia

  29. Sharp Asymptotic Theory of Maximum Likelihood Estimation for Gaussian Processes with an RBF Kernel

    Oct 7, 2026Ameer Qaqish, Sudipto Banerjee, Didong LiGaussian ProcessesKernel Methods

    Department of Biostatistics, University of North Carolina at Chapel Hill · Department of Biostatistics, University of California, Los Angeles

  30. Unbounded Characteristic and Universal Kernels

    Oct 7, 2026Jose Cribeiro-Ramallo, Florian Kalinke, Zoltán SzabóKernel MethodsReproducing Kernel Hilbert Spaces

    Karlsruhe Institute of Technology · London School of Economics