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. When do cheap embeddings beat protein language models? A theoretically-grounded hashing sketch for biological sequence classification

    Dec 10, 2025Sarwan Ali, Taslim Murad, Imdadullah Khan +1Protein Language ModelsRandomized Sketching

    Columbia University, NY, USA · Institute of Business Administration, Karachi, Pakistan · Lahore University of Management Sciences, Lahore, Pakistan +1

  2. Classical and quantum kernel fusion for two-sample testing

    Nov 26, 2025Yu Terada, Yugo Ogio, Ken Arai +2Two-Sample TestingMaximum Mean Discrepancy

    Advanced Research Laboratory, Sony Group Corporation, 1-7-1 Konan, Minato-ku, Tokyo 108-0075, Japan · Deloitte Tohmatsu Consulting LLC.

  3. Sparse, self-organizing ensembles of local kernels detect rare statistical anomalies

    Nov 5, 2025Gaia Grosso, Sai Sumedh R. Hindupur, Thomas Fel +3Kernel MethodsSemi-Supervised Anomaly Detection

    NSF AI Institute for Artificial Intelligence and Fundamental Interactions, Cambridge, 02139, MA · Laboratory for Nuclear Science, Massachusetts Institute of Technology, Cambridge, 02139, MA · School of Engineering and Applied Sciences, Harvard University, Allston, 02134, MA +1

  4. Manifold Approximation leads to Robust Kernel Alignment

    Oct 27, 2025Mohammad Tariqul Islam, Du Liu, Deblina SarkarKernel MethodsRepresentational Similarity Analysis

    MIT

  5. Theoretical Refinement of CLIP by Utilizing Linear Structure of Optimal Similarity

    Oct 17, 2025Naoki Yoshida, Satoshi Hayakawa, Yuhta Takida +3Cross-Modal LearningKernel Mean Embeddings

    The University of Tokyo · Sony Group Corporation · Sony AI

  6. Predicting kernel regression learning curves from only raw data statistics

    Oct 16, 2025Dhruva Karkada, Joseph Turnbull, Yuxi Liu +1Kernel RegressionKernel Methods

    UC Berkeley · Imbue

  7. Data-efficient Kernel Methods for Learning Hamiltonian Systems

    Sep 21, 2025Yasamin Jalalian, Mostafa Samir, Boumediene Hamzi +2Dynamical SystemsNonlinear System Identification

    Department of Computing and Mathematical Sciences, Caltech, CA, USA. · Beyond Limits, CA, USA. · The Alan Turing Institute, London, UK. +1

  8. A Compositional Kernel Model for Feature Learning

    Sep 17, 2025Feng Ruan, Keli Liu, Michael JordanKernel Ridge RegressionKernel Regression

    *Department of Statistics and Data Science, Northwestern University. · *Company E · *Department of Statistics and Computer Science, University of California, Berkeley.

  9. A Kernel-based Stochastic Approximation Framework for Nonlinear Operator Learning

    Sep 14, 2025Jia-Qi Yang, Lei ShiStochastic ApproximationReproducing Kernel Hilbert Spaces

    School of Mathematical Sciences and Shanghai Key Laboratory for Contemporary Applied Mathematics, Fudan University, Shanghai 200433, China.

  10. Signature-Kernel One-Class Learning with Manifold Regularisation for Multivariate Time Series Anomaly Detection

    Jul 31, 2025Shervin Rahimzadeh ArashlooTime-Series Anomaly DetectionOne-Class Classification

    Department of Computer Engineering, Faculty of Engineering, Bilkent University, Ankara, Turkey.

  11. The kernel of graph indices for vector search

    Jun 25, 2025Mariano Tepper, Ted WillkeGraph-Based RetrievalKernel Methods

    IBM WatsonX

  12. Gaussian Processes and Reproducing Kernel Hilbert Spaces: Connections and Equivalences

    Jun 20, 2025Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic +1Gaussian ProcessesReproducing Kernel Hilbert Spaces

    EURECOM · University of Tübingen, Tübingen AI Center Max Planck Institute for Intelligent Systems · Adelaide University Australian Institute for Machine Learning +1

  13. Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation

    May 21, 2025Kun Fang, Qinghua Tao, Mingzhen He +6Principal Component AnalysisKernel Methods

    Department of Automation, Shanghai Jiao Tong University · Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University · School of Automation, Beijing Institute of Technology +2

  14. Out-of-Sample Embedding with Proximity Data: Projection versus Restricted Reconstruction

    May 10, 2025Michael W. Trosset, Kaiyi Tan, Minh Tang +1Multidimensional ScalingKernel Methods

    Department of Statistics, Indiana University. · Department of Statistics, North Carolina State University. · Department of Applied Mathematics & Statistics, Johns Hopkins University.

  15. Benign Overfitting with Quantum Kernels

    Mar 21, 2025Joachim Tomasi, Sandrine Anthoine, Hachem KadriBenign OverfittingQuantum Machine Learning

    Aix-Marseille Univ, CNRS, LIS, Marseille, France. · Aix Marseille Univ, CNRS, I2M, Marseille, France.

  16. A Mechanistic Interpretation of Prescribed-Parameter Convolutional Kernels

    Feb 24, 2025Jorge Marco Blanco, Rubén Cuevas RumínTime Series ClassificationKernel Methods

    Universidad Carlos III de Madrid, Spain · UC3M-Santander Big Data Institute, Spain

  17. Physics-Informed Support Vector Kernels via Green-Function Analogies and Jackson-Chebyshev Spectral Design

    Feb 16, 2025Nan-Hong Kuo, Renata WongSupport Vector MachinesKernel Methods

    Department of Physics, National Taiwan University, Taipei, Taiwan · Department of Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan · Department of Neurology, Chang Gung Memorial Hospital, Keelung, Taiwan +1

  18. Fixed-Gaussian Spectral Algorithms: Minimax Optimal Rates for Misspecified Learning and Transfer

    Jan 18, 2025Haotian Lin, Matthew ReimherrSpectral RegularizationConcept Drift

    Department of Statistics, The Pennsylvania State University.

  19. Conditional Moment Kernels: Joint Modeling of Expected Returns and Covariances in Unbalanced Panels

    Oct 29, 2024Damir Filipovic, Paul SchneiderQuantitative FinanceCovariance Estimation

    École Polytechnique Fédérale de Lausanne and Swiss Finance Institute · Università della Svizzera italiana and Swiss Finance Institute

  20. Which Spaces can be Embedded in LpL_p-type Reproducing Kernel Banach Space? A Characterization via Metric Entropy

    Oct 14, 2024Yiping Lu, Daozhe Lin, Qiang DuReproducing Kernel Hilbert SpacesKernel Methods

    Industrial Engineering & Management Sciences, Northwestern University · Department of Applied Physics & Applied Mathematics, Columbia University

  21. Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent

    Sep 13, 2024Sayan Banerjee, Krishnakumar Balasubramanian, Promit GhosalKernel MethodsVariational Inference

    Department of Statistics and Operations Research, University of North Carolina, Chapel Hill. · Department of Statistics, University of California, Davis. · Department of Statistics, University of Chicago.

  22. Nonparametric Control Koopman Operators

    May 12, 2024Petar Bevanda, Bas Driessen, Lucian Cristian Iacob +3Koopman Operator LearningSystem Identification

    Chair of Information-oriented Control, TU München, Germany · Control Systems Group, TU/e, Eindhoven, The Netherlands · Systems and Control Laboratory, HUNREN Institute for Computer Science and Control, Budapest, Hungary +2

  23. Consistent Distributed Ranking of Generative Models via Kernel Distances

    Oct 18, 2023Zixiao Wang, Farzan Farnia, Zhenghao Lin +2Kernel MethodsImage Generation Evaluation

  24. Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations

    Dec 23, 2018Balázs Csanád Csáji, Krisztián Balázs KisConfidence Region EstimationUncertainty Quantification

    EPIC Centre of Excellence MTA SZTAKI: Institute for Computer Science and Control Hungarian Academy of Sciences, Budapest, Hungary