mlsanityData to 7 Oct 2026

Quadrature-based features for kernel approximation

arXiv 1802.03832 ↗First posted 11 Feb 2018cs.LG stat.MLcited by 25

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Abstract (arXiv)

We consider the problem of improving kernel approximation via randomized feature maps. These maps arise as Monte Carlo approximation to integral representations of kernel functions and scale up kernel methods for larger datasets. Based on an efficient numerical integration technique, we propose a unifying approach that reinterprets the previous random features methods and extends to better estimates of the kernel approximation. We derive the convergence behaviour and conduct an extensive empirical study that supports our hypothesis.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Feb 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 5; 2023: 5; 2024: 5; 2025: 1; 2026: 2.

25 papers.

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  1. 2023Parameter-Efficient Orthogonal Finetuning via Butterfly Factorizationcited by 112
  2. 2022Monarch: Expressive Structured Matrices for Efficient and Accurate Trainingcited by 100
  3. 2021Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Modelscited by 75
  4. 2023Simple Hardware-Efficient Long Convolutions for Sequence Modelingcited by 62
  5. 2022Accelerated Linearized Laplace Approximation for Bayesian Deep Learningcited by 27
  6. 2022NeuralEF: Deconstructing Kernels by Deep Neural Networkscited by 26
  7. 2021No-Regret Algorithms for Private Gaussian Process Bandit Optimizationcited by 11
  8. 2024Fast Summation of Radial Kernels via QMC Slicingcited by 7
  9. 2021Gauss-Legendre Features for Gaussian Process Regressioncited by 5
  10. 2023Uni-Fusion: Universal Continuous Mappingcited by 5
  11. 2024Spectraformer: A Unified Random Feature Framework for Transformercited by 3
  12. 2021Efficient Identification of Butterfly Sparse Matrix Factorizationscited by 2
13 more
  1. 2024Variance-Reducing Couplings for Random Featurescited by 2
  2. 2024On the design of scalable, high-precision spherical-radial Fourier featurescited by 2
  3. 2021Denoising Score Matching with Random Fourier Featurescited by 1
  4. 2021Towards Unbiased Random Features with Lower Variance For Stationary Indefinite Kernelscited by 1
  5. 2022Arithmetic Circuits, Structured Matrices and (not so) Deep Learningcited by 1
  6. 2023Trigonometric Quadrature Fourier Features for Scalable Gaussian Process Regressioncited by 1
  7. 2025Feature maps for the Laplacian kernel and its generalizationscited by 1
  8. 2021Fast Newton method solving KLR based on Multilevel Circulant Matrix with log-linear complexitycited by 0
  9. 2022Robust representations of oil wells' intervals via sparse attention mechanismcited by 0
  10. 2023Machine Learning Discovery of Optimal Quadrature Rules for Isogeometric Analysiscited by 0
  11. 2024Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matricescited by 0
  12. 2026Regular Fourier Features for Nonstationary Gaussian Processescited by 0
  13. 2026New Bounds for Kernel Sums via Fast Spherical Embeddingscited by 0