kernel ridge regression

Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning ICML 2022 Learning Curves for Gaussian Process Regression with Power-Law Priors and Targets ICLR 2022 Neural Fields As Learnable Kernels for 3D Reconstruction CVPR 2022 On the Benefits of Large Learning Rates for Kernel Methods COLT 2022 Optimal Rates for Regularized Conditional Mean Embedding Learning NeurIPS 2022 Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression NeurIPS 2022 Random Gegenbauer Features for Scalable Kernel Methods ICML 2022 Reconstruction on Trees and Low-Degree Polynomials NeurIPS 2022 Sketching Algorithms and Lower Bounds for Ridge Regression ICML 2022 Target alignment in truncated kernel ridge regression NeurIPS 2022 Variational Gaussian Processes: A Functional Analysis View AISTATS 2022 Accumulations of Projections—A Unified Framework for Random Sketches in Kernel Ridge Regression AISTATS 2021 Dataset Meta-Learning from Kernel Ridge-Regression ICLR 2021 Deconditional Downscaling with Gaussian Processes NeurIPS 2021 Distributed Nyström Kernel Learning with Communications ICML 2021 Effective Distributed Learning with Random Features: Improved Bounds and Algorithms ICLR 2021 Fast Rates for Structured Prediction COLT 2021 Fast Statistical Leverage Score Approximation in Kernel Ridge Regression AISTATS 2021 Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime NeurIPS 2021 Generalization Properties of hyper-RKHS and its Applications JMLR 2021 How rotational invariance of common kernels prevents generalization in high dimensions ICML 2021 Kernel Continual Learning ICML 2021 Kernel regression in high dimensions: Refined analysis beyond double descent AISTATS 2021 Optimal Rates of Distributed Regression with Imperfect Kernels JMLR 2021 ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions NeurIPS 2021