mlsanityData to 7 Oct 2026

A la Carte - Learning Fast Kernels

arXiv 1412.6493 ↗First posted 19 Dec 2014cs.LG stat.MLcited by 18

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Abstract (arXiv)

Kernel methods have great promise for learning rich statistical representations of large modern datasets. However, compared to neural networks, kernel methods have been perceived as lacking in scalability and flexibility. We introduce a family of fast, flexible, lightly parametrized and general purpose kernel learning methods, derived from Fastfood basis function expansions. We provide mechanisms to learn the properties of groups of spectral frequencies in these expansions, which require only O(mlogd) time and O(m) memory, for m basis functions and d input dimensions. We show that the proposed methods can learn a wide class of kernels, outperforming the alternatives in accuracy, speed, and memory consumption.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Dec 2014.

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Citing papers per year: 2021: 5; 2022: 4; 2023: 3; 2024: 2; 2025: 2; 2026: 2.

18 papers.

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  1. 2022Posterior and Computational Uncertainty in Gaussian Processescited by 23
  2. 2022Overcoming the Spectral Bias of Neural Value Approximationcited by 18
  3. 2021On Learning the Transformer Kernelcited by 14
  4. 2023Large-Scale Gaussian Processes via Alternating Projectioncited by 11
  5. 2022Low-Precision Arithmetic for Fast Gaussian Processescited by 10
  6. 2024Hyperparameter Optimization for Randomized Algorithms: A Case Study on Random Featurescited by 7
  7. 2023Collapsed Inference for Bayesian Deep Learningcited by 6
  8. 2023Bayesian Non-linear Latent Variable Modeling via Random Fourier Featurescited by 4
  9. 2021GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Datacited by 3
  10. 2021Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefitscited by 3
  11. 2024Spectraformer: A Unified Random Feature Framework for Transformercited by 3
  12. 2026Spectral methods: crucial for machine learning, natural for quantum computers?cited by 3
6 more
  1. 2021MetaKernel: Learning Variational Random Features with Limited Labelscited by 2
  2. 2021StreaMRAK a Streaming Multi-Resolution Adaptive Kernel Algorithmcited by 0
  3. 2022RFFNet: Large-Scale Interpretable Kernel Methods via Random Fourier Featurescited by 0
  4. 2025Scalable Deep Basis Kernel Gaussian Processescited by 0
  5. 2025Unbiased Stochastic Optimization for Gaussian Processes on Finite Dimensional RKHScited by 0
  6. 2026Long Range Frequency Tuning for QMLcited by 0