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Kernel Methods

73 papers

Papers per year

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Papers

Bridging the Gap Between Hyperdimensional Computing and Kernel Methods via the Nyström Method AAAI 2025
A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression NIPS 2024
Inverse M-Kernels for Linear Universal Approximators of Non-Negative Functions NIPS 2024
Statistical and Geometrical properties of the Kernel Kullback-Leibler divergence NIPS 2024
Learning to Embed Distributions via Maximum Kernel Entropy NIPS 2024
Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality NIPS 2024
Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms NIPS 2024
Graph Learning for Numeric Planning NIPS 2024
Graph Convolutional Kernel Machine versus Graph Convolutional Networks NIPS 2023
Deep learning with kernels through RKHM and the Perron-Frobenius operator NIPS 2023
Positively Weighted Kernel Quadrature via Subsampling NIPS 2022
Distributed Learning of Conditional Quantiles in the Reproducing Kernel Hilbert Space NIPS 2022
Fast Instrument Learning with Faster Rates NIPS 2022
Efficient Aggregated Kernel Tests using Incomplete $U$-statistics NIPS 2022
Target alignment in truncated kernel ridge regression NIPS 2022
FourierFormer: Transformer Meets Generalized Fourier Integral Theorem NIPS 2022
An analysis of Ermakov-Zolotukhin quadrature using kernels NIPS 2021
Neural Splines: Fitting 3D Surfaces With Infinitely-Wide Neural Networks CVPR 2021
Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method NIPS 2020
Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces NIPS 2020
A Fair Classifier Using Kernel Density Estimation NIPS 2020
Finite Versus Infinite Neural Networks: an Empirical Study NIPS 2020
A kernel test for quasi-independence NIPS 2020
Reservoir Computing meets Recurrent Kernels and Structured Transforms NIPS 2020
Statistical Optimal Transport posed as Learning Kernel Embedding NIPS 2020