Co-occurring keywords · papers mentioning both
Papers
Towards Theoretical Understanding of Learning Large-scale Dependent Data via Random Features
ICML 2024
A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression
NeurIPS 2023
Differentially Private Nonlinear Causal Discovery from Numerical Data
AAAI 2023
Gradient Boosting Performs Gaussian Process Inference
ICLR 2023
Kernelized Reinforcement Learning with Order Optimal Regret Bounds
NeurIPS 2023
Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective
NeurIPS 2023
On the Asymptotic Learning Curves of Kernel Ridge Regression under Power-law Decay
NeurIPS 2023
On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression Estimators
JMLR 2023
On the Optimality of Misspecified Kernel Ridge Regression
ICML 2023
On the Saturation Effect of Kernel Ridge Regression
ICLR 2023
On the Size and Approximation Error of Distilled Datasets
NeurIPS 2023
On the Sublinear Regret of GP-UCB
NeurIPS 2023
Overparameterized Random Feature Regression with Nearly Orthogonal Data
AISTATS 2023
Robustifying Generalizable Implicit Shape Networks with a Tunable Non-Parametric Model
NeurIPS 2023
Sample Complexity of Kernel-Based Q-Learning
AISTATS 2023
The Exact Sample Complexity Gain from Invariances for Kernel Regression
NeurIPS 2023
Towards Understanding Ensemble Distillation in Federated Learning
ICML 2023
Data-splitting improves statistical performance in overparameterized regimes
AISTATS 2022
Distributed Randomized Sketching Kernel Learning
AAAI 2022
Distribution Regression with Sliced Wasserstein Kernels
ICML 2022
Efficient Dataset Distillation using Random Feature Approximation
NeurIPS 2022
Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression
AISTATS 2022
Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data
ICML 2022
Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression
JMLR 2022
Implicit kernel meta-learning using kernel integral forms
UAI 2022