Random Feature Methods

Ask about this topic

Overview

Random feature methods replace expensive kernel computations or trained hidden layers with finite sets of randomized functions, making learning more scalable. Approaches include random Fourier and spectral features, structured projections, graph-based features, and adaptive or learned feature distributions, often paired with ridge regression or least-squares readouts. They are studied in kernel regression and classification, graph learning, PDE solving, time-series modeling, and attention, with theory covering approximation, learning rates, and robustness. A central challenge is balancing feature count and computational cost against accuracy and numerical conditioning, especially when data are misspecified or have exploitable structure.

Latest papers

CardsList

48 papers11 papers in the last four weeks, up 83% on the four weeks before. 0.1% of all new papers.

  1. Conditional KRR: Injecting Unpenalized Features into Kernel Methods with Applications to Kernel Thresholding

    May 25, 2026Rustem Takhanov, Zhenisbek AssylbekovKernel Ridge RegressionKernel Regression

    Department of Mathematics, Nazarbayev University, Astana, Kazakhstan · Nazarbayev University Research Administration, Astana, Kazakhstan · Purdue University Fort Wayne, Indiana, USA.

  2. Worse than Random: The Importance of a Baseline for Unsupervised Feature Selection -- Extended Version

    May 21, 2026Muhammad Rajabinasab, Michael E. Houle, Oussama Chelly +1Unsupervised LearningRandom Feature Methods

    University of Southern Denmark, Odense, Denmark · New Jersey Institute of Technology, Newark, NJ, United States · Oratio Technologies, Tunis, Tunisia

  3. Randomized Atomic Feature Models for Physics-Informed Identification of Dynamic Systems

    May 14, 2026Rajiv Singh, Mario Sznaier, Lennart LjungRandom Feature MethodsSystem 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

  4. Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentation

    May 11, 2026Lucas Morisset, Alain Durmus, Adrien HardyNeural Network GeneralizationHigh-Dimensional Asymptotics

    Qube Research & Technologies · École Polytechnique

  5. Efficient Techniques for Data Reconstruction, with Finite-Width Recovery Guarantees

    May 7, 2026Edward Tansley, Roy Makhlouf, Estelle Massart +1Training Data Reconstruction AttacksRandom Feature Methods

    Mathematical Institute, Woodstock Road, University of Oxford, Oxford, UK, OX2 6GG. · ICTEAM Institute, UCLouvain, Euler Building, Avenue Georges Lemaˆıtre, 4 - bte L4.05.01, Louvain-la-Neuve, B - 1348, Belgium.

  6. Fourier Feature Methods for Nonlinear Causal Discovery: FFML Scoring, TRFF Scoring, and FFCI Testing in Mixed Data

    May 7, 2026Joseph D. RamseyFourier Feature EmbeddingsCausal Discovery

    Department of Philosophy Carnegie Mellon University Pittsburgh, PA 15213

  7. Green-ELM: Efficient Analytic Learning via High-Dimensional Random Projections

    Apr 17, 2026Wladimir SilvaNeural Network OptimizationRandom Feature Methods

    Department of Computer Engineering North Carolina State University

  8. Loop Corrections in Random Feature Models: Training Error and Generalization Gap

    Apr 14, 2026Taeyoung KimRandom Feature MethodsKernel Ridge Regression

    Center for AI and Natural Sciences, Korea Institute for Advanced Study, Seoul 02455, South Korea

  9. A Little Rank Goes a Long Way: Random Scaffolds with LoRA Adapters Are All You Need

    Apr 9, 2026Hananel Hazan, Yanbo Zhang, Benedikt Hartl +1Random Feature MethodsParameter-Efficient Fine-Tuning

    Allen Discovery Center at Tufts University, Medford, Massachusetts, USA · Institute for Theoretical Physics, TU Wien, Wien, Austria · Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, USA

  10. Asymptotic Learning Curves for Diffusion Models with Random Features Score and Manifold Data

    Mar 24, 2026Anand Jerry George, Nicolas MacrisIntrinsic DimensionalityRandom Feature Methods

    École Polytechnique Fédérale de Lausanne (EPFL), Lab for Statistical Mechanics of Inference in Large Systems (SMILS), CH-1015 Lausanne, Switzerland

  11. Power-Law Spectrum of the Random Feature Model

    Mar 15, 2026Elliot Paquette, Ke Liang Xiao, Yizhe ZhuRandom Feature Methods

    Department of Mathematics and Statistics, McGill University · Department of Mathematics, University of Southern California

  12. Improved Scaling Laws via Weak-to-Strong Generalization in Random Feature Ridge Regression

    Mar 5, 2026Diyuan Wu, Lehan Chen, Theodor Misiakiewicz +1Weak-to-Strong GeneralizationScaling Laws

    Institute of Science and Technology Austria (ISTA) · Department of Applied and Computational Mathematics, Yale University · Department of Statistics and Data Science, Yale University

  13. How Does the ReLU Activation Affect the Implicit Bias of Gradient Descent on High-dimensional Neural Network Regression?

    Mar 5, 2026Kuo-Wei Lai, Guanghui Wang, Molei Tao +1High-Dimensional Linear RegressionRandom Feature Methods

    School of Electrical and Computer Engineering, Georgia Institute of Technology · College of Computing, Georgia Institute of Technology · School of Mathematics, Georgia Institute of Technology +1

  14. Scaling Laws of SignSGD in Linear Regression: When Does It Outperform SGD?

    Mar 2, 2026Jihwan Kim, Dogyoon Song, Chulhee YunSign-Based OptimizationRandom Feature Methods

    Seoul National University & KAIST InnoCORE LLM · University of California, Davis · KAIST

  15. Random Features for Operator-Valued Kernels: Bridging Kernel Methods and Neural Operators

    Mar 1, 2026Mike Nguyen, Nicole MückeRandom Feature MethodsKernel Methods

    Technical University of Braunschweig

  16. Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features

    Feb 22, 2026Michele Caprio, Katerina Papagiannouli, Siu Lun Chau +1Random Feature MethodsBayesian Inference

    The University of Manchester, UK · University of Pisa, Italy · Nanyang Technological University, Singapore +1

  17. Random Wavelet Features for Graph Kernel Machines

    Feb 17, 2026Valentin de Bassompierre, Jean-Charles Delvenne, Laurent JacquesGraph Representation LearningGraph Embedding

    INMA/ICTEAM, UCLouvain, Belgium

  18. Computationally-efficient Graph Modeling with Refined Graph Random Features

    Oct 9, 2025Krzysztof Choromanski, Avinava Dubey, Arijit Sehanobish +1Graph Representation LearningRandomized Sketching

    Google DeepMind, New York, NY, USA · Columbia University, New York, NY, USA · Google Research, Mountain View, CA, USA +2