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kernel ridge regression

111 papers

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Also known as

KRR

Co-occurring keywords

kernel methods (1097) reproducing kernel hilbert space (334) generalization error (285) distributed learning (563) gaussian process (1200) gradient descent (1144) kernel approximation (100) nystrom method (46) convergence rate (607) minimax optimality (76)

Papers

The Exact Sample Complexity Gain from Invariances for Kernel Regression NIPS 2023
Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective NIPS 2023
Variational Gaussian Processes: A Functional Analysis View AISTATS 2022
Target alignment in truncated kernel ridge regression NIPS 2022
Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression JMLR 2022
Implicit kernel meta-learning using kernel integral forms UAI 2022
Neural Fields As Learnable Kernels for 3D Reconstruction CVPR 2022
Efficient Dataset Distillation using Random Feature Approximation NIPS 2022
Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression NIPS 2022
Optimal Rates for Regularized Conditional Mean Embedding Learning NIPS 2022
On the Benefits of Large Learning Rates for Kernel Methods COLT 2022
Data-splitting improves statistical performance in overparameterized regimes AISTATS 2022
Sketching Algorithms and Lower Bounds for Ridge Regression ICML 2022
Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data ICML 2022
Distribution Regression with Sliced Wasserstein Kernels ICML 2022
Distributed Randomized Sketching Kernel Learning AAAI 2022
Random Gegenbauer Features for Scalable Kernel Methods ICML 2022
Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression AISTATS 2022
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning ICML 2022
Reconstruction on Trees and Low-Degree Polynomials NIPS 2022
Kernel regression in high dimensions: Refined analysis beyond double descent AISTATS 2021
Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network AISTATS 2021
Deconditional Downscaling with Gaussian Processes NIPS 2021
Distributed Nyström Kernel Learning with Communications ICML 2021
How rotational invariance of common kernels prevents generalization in high dimensions ICML 2021