Kernel Methods

Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization UAI 2026 Bridging the Gap Between Hyperdimensional Computing and Kernel Methods via the Nyström Method AAAI 2025 Extending Mercer's expansion to indefinite and asymmetric kernels ICLR 2025 A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression NeurIPS 2024 Graph Learning for Numeric Planning NeurIPS 2024 Inverse M-Kernels for Linear Universal Approximators of Non-Negative Functions NeurIPS 2024 Learning to Embed Distributions via Maximum Kernel Entropy NeurIPS 2024 Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms NeurIPS 2024 Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality NeurIPS 2024 Statistical and Geometrical properties of the Kernel Kullback-Leibler divergence NeurIPS 2024 Deep learning with kernels through RKHM and the Perron-Frobenius operator NeurIPS 2023 Graph Convolutional Kernel Machine versus Graph Convolutional Networks NeurIPS 2023 Distributed Learning of Conditional Quantiles in the Reproducing Kernel Hilbert Space NeurIPS 2022 Efficient Aggregated Kernel Tests using Incomplete U-statistics NeurIPS 2022 Fast Instrument Learning with Faster Rates NeurIPS 2022 FourierFormer: Transformer Meets Generalized Fourier Integral Theorem NeurIPS 2022 Positively Weighted Kernel Quadrature via Subsampling NeurIPS 2022 Target alignment in truncated kernel ridge regression NeurIPS 2022 An analysis of Ermakov-Zolotukhin quadrature using kernels NeurIPS 2021 Neural Splines: Fitting 3D Surfaces With Infinitely-Wide Neural Networks CVPR 2021 A Fair Classifier Using Kernel Density Estimation NeurIPS 2020 A kernel test for quasi-independence NeurIPS 2020 Finite Versus Infinite Neural Networks: an Empirical Study NeurIPS 2020 Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method NeurIPS 2020 Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces NeurIPS 2020