kernel approximation

Simplex Random Features ICML 2023 Tanimoto Random Features for Scalable Molecular Machine Learning NeurIPS 2023 Tensor-based Kernel Machines with Structured Inducing Points for Large and High-Dimensional Data AISTATS 2023 A Perturbation-Based Kernel Approximation Framework JMLR 2022 Chefs' Random Tables: Non-Trigonometric Random Features NeurIPS 2022 Decentralized Learning for Overparameterized Problems: A Multi-Agent Kernel Approximation Approach ICLR 2022 Gauss-Legendre Features for Gaussian Process Regression JMLR 2022 How Good Are Low-Rank Approximations in Gaussian Process Regression? AAAI 2022 Hybrid Random Features ICLR 2022 Leverage Score Sampling for Tensor Product Matrices in Input Sparsity Time ICML 2022 Random Gegenbauer Features for Scalable Kernel Methods ICML 2022 Weighted Gaussian Process Bandits for Non-stationary Environments AISTATS 2022 Distributed Nyström Kernel Learning with Communications ICML 2021 Fast and Compact Bilinear Pooling by Shifted Random Maclaurin AAAI 2021 Fast Sketching of Polynomial Kernels of Polynomial Degree ICML 2021 Local Differential Privacy for Bayesian Optimization AAAI 2021 Memory and Computation-Efficient Kernel SVM via Binary Embedding and Ternary Model Coefficients AAAI 2021 No-Regret Algorithms for Private Gaussian Process Bandit Optimization AISTATS 2021 One-Sketch-for-All: Non-linear Random Features from Compressed Linear Measurements AISTATS 2021 Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition ICML 2021 What can linearized neural networks actually say about generalization? NeurIPS 2021 Fourier Sparse Leverage Scores and Approximate Kernel Learning NeurIPS 2020 Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features ICML 2020 Multipole Graph Neural Operator for Parametric Partial Differential Equations NeurIPS 2020 Orlicz Random Fourier Features JMLR 2020