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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

How Good Are Inducing Points for Dataset Distillation? (Student Abstract) AAAI 2026
Enhanced Feature Learning via Regularisation: Integrating Neural Networks and Kernel Methods JMLR 2025
Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos JMLR 2025
Optimal Rates of Kernel Ridge Regression under Source Condition in Large Dimensions JMLR 2025
ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models CVPR 2025
Universality of Kernel Random Matrices and Kernel Regression in the Quadratic Regime JMLR 2025
On the Saturation Effects of Spectral Algorithms in Large Dimensions NIPS 2024
Provable and Efficient Dataset Distillation for Kernel Ridge Regression NIPS 2024
Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms NIPS 2024
A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression NIPS 2024
Supervised Kernel Thinning NIPS 2024
Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality NIPS 2024
Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm NIPS 2024
Sample Complexity of Kernel-Based Q-Learning AISTATS 2023
On the Size and Approximation Error of Distilled Datasets NIPS 2023
Kernelized Reinforcement Learning with Order Optimal Regret Bounds NIPS 2023
Towards Understanding Ensemble Distillation in Federated Learning ICML 2023
On the Optimality of Misspecified Kernel Ridge Regression ICML 2023
On the Sublinear Regret of GP-UCB NIPS 2023
On the Asymptotic Learning Curves of Kernel Ridge Regression under Power-law Decay NIPS 2023
Overparameterized Random Feature Regression with Nearly Orthogonal Data AISTATS 2023
On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression Estimators JMLR 2023
Differentially Private Nonlinear Causal Discovery from Numerical Data AAAI 2023
A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression NIPS 2023
Robustifying Generalizable Implicit Shape Networks with a Tunable Non-Parametric Model NIPS 2023