Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm

23 February 2017

Papers citing "Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm"

50 / 105 papers shown

Title
Sobolev norm inconsistency of kernel interpolation Yunfei Yang 34 0 0 29 Apr 2025
On the Saturation Effects of Spectral Algorithms in Large Dimensions Weihao Lu Haobo Zhang Yicheng Li Q. Lin 44 1 0 01 Mar 2025
Nested Expectations with Kernel Quadrature Zonghao Chen Masha Naslidnyk F. Briol 36 0 0 25 Feb 2025
On strong posterior contraction rates for Besov-Laplace priors in the white noise model Emanuele Dolera Stefano Favaro Matteo Giordano 26 0 0 11 Nov 2024
A Simple Model of Inference Scaling Laws Noam Levi LRM 32 6 0 21 Oct 2024
Diffusion-based Semi-supervised Spectral Algorithm for Regression on Manifolds Weichun Xia Jiaxin Jiang Lei Shi 26 0 0 18 Oct 2024
Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups Vladimir Kostic Karim Lounici Helene Halconruy Timothée Devergne P. Novelli Massimiliano Pontil 36 0 0 18 Oct 2024
Which Spaces can be Embedded in $L_p$ -type Reproducing Kernel Banach Space? A Characterization via Metric Entropy Yiping Lu Daozhe Lin Qiang Du 39 0 0 14 Oct 2024
On the Impacts of the Random Initialization in the Neural Tangent Kernel Theory Guhan Chen Yicheng Li Qian Lin AAML 38 1 0 08 Oct 2024
Distributed Learning with Discretely Observed Functional Data Jiading Liu Lei Shi 20 0 0 03 Oct 2024
$Gaussian kernel expansion with basis functions uniformly bounded in $\mathcal{L}_{\infty}$$ Gaussian kernel expansion with basis functions uniformly bounded in $\mathcal{L}_{\infty}$ M. Bisiacco G. Pillonetto 20 0 0 02 Oct 2024
On the Pinsker bound of inner product kernel regression in large dimensions Weihao Lu Jialin Ding Haobo Zhang Qian Lin 57 0 0 02 Sep 2024
Improving Adaptivity via Over-Parameterization in Sequence Models Yicheng Li Qian Lin 31 1 0 02 Sep 2024
Operator World Models for Reinforcement Learning P. Novelli Marco Prattico Massimiliano Pontil C. Ciliberto OffRL 42 0 0 28 Jun 2024
Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms Dimitri Meunier Zikai Shen Mattes Mollenhauer Arthur Gretton Zhu Li 51 5 0 23 May 2024
Learning the Infinitesimal Generator of Stochastic Diffusion Processes Vladimir Kostic Karim Lounici Helene Halconruy Timothée Devergne Massimiliano Pontil DiffM 40 4 0 21 May 2024
On the Saturation Effect of Kernel Ridge Regression Yicheng Li Haobo Zhang Qian Lin 25 19 0 15 May 2024
The phase diagram of kernel interpolation in large dimensions Haobo Zhang Weihao Lu Qian Lin 60 5 0 19 Apr 2024
On Safety in Safe Bayesian Optimization Christian Fiedler Johanna Menn Lukas Kreisköther Sebastian Trimpe 33 9 0 19 Mar 2024
On the Approximation of Kernel functions Paul Dommel Alois Pichler 31 0 0 11 Mar 2024
Asymptotic Theory for Linear Functionals of Kernel Ridge Regression Rui Tuo Lu Zou 29 0 0 07 Mar 2024
Spectral Algorithms on Manifolds through Diffusion Weichun Xia Lei Shi 23 1 0 06 Mar 2024
Smoothness Adaptive Hypothesis Transfer Learning Haotian Lin M. Reimherr 25 6 0 22 Feb 2024
Practical Kernel Tests of Conditional Independence Roman Pogodin Antonin Schrab Yazhe Li Danica J. Sutherland Arthur Gretton 38 5 0 20 Feb 2024
A Bound on the Maximal Marginal Degrees of Freedom Paul Dommel 19 1 0 20 Feb 2024
The Optimality of Kernel Classifiers in Sobolev Space Jianfa Lai Zhifan Li Dongming Huang Qian Lin 27 1 0 02 Feb 2024
Spectrally Transformed Kernel Regression Runtian Zhai Rattana Pukdee Roger Jin Maria-Florina Balcan Pradeep Ravikumar BDL 28 2 0 01 Feb 2024
Generalization Error Curves for Analytic Spectral Algorithms under Power-law Decay Yicheng Li Weiye Gan Zuoqiang Shi Qian Lin 41 5 0 03 Jan 2024
Generalization in Kernel Regression Under Realistic Assumptions Daniel Barzilai Ohad Shamir 35 15 0 26 Dec 2023
Consistent Long-Term Forecasting of Ergodic Dynamical Systems Prune Inzerilli Vladimir Kostic Karim Lounici P. Novelli Massimiliano Pontil 18 4 0 20 Dec 2023
Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm Zhu Li Dimitri Meunier Mattes Mollenhauer Arthur Gretton 39 6 0 12 Dec 2023
Adaptive Parameter Selection for Kernel Ridge Regression Shao-Bo Lin 11 3 0 10 Dec 2023
Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling Antoine Chatalic Nicolas Schreuder Ernesto De Vito Lorenzo Rosasco 27 3 0 22 Nov 2023
On the Asymptotic Learning Curves of Kernel Ridge Regression under Power-law Decay Yicheng Li Hao Zhang Qian Lin 35 12 0 23 Sep 2023
Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos Di Wang Xiaotong Liu Shao-Bo Lin Ding-Xuan Zhou 33 0 0 08 Sep 2023
Kernel Single Proxy Control for Deterministic Confounding Liyuan Xu Arthur Gretton CML 26 2 0 08 Aug 2023
Nonlinear Meta-Learning Can Guarantee Faster Rates Dimitri Meunier Zhu Li Arthur Gretton Samory Kpotufe 27 6 0 20 Jul 2023
Estimating Koopman operators with sketching to provably learn large scale dynamical systems Giacomo Meanti Antoine Chatalic Vladimir Kostic P. Novelli Massimiliano Pontil Lorenzo Rosasco 21 9 0 07 Jun 2023
How many samples are needed to leverage smoothness? Vivien A. Cabannes Stefano Vigogna 8 2 0 25 May 2023
On the Optimality of Misspecified Kernel Ridge Regression Haobo Zhang Yicheng Li Weihao Lu Qian Lin 47 13 0 12 May 2023
Supervised learning with probabilistic morphisms and kernel mean embeddings H. Lê GAN 25 1 0 10 May 2023
On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains Yicheng Li Zixiong Yu Y. Cotronis Qian Lin 55 13 0 04 May 2023
Convergence and error analysis of PINNs Nathan Doumèche Gérard Biau D. Boyer PINN AI4CE 45 17 0 02 May 2023
$Toward $L_\infty$-recovery of Nonlinear Functions: A Polynomial Sample Complexity Bound for Gaussian Random Fields$ Toward $L_\infty$ -recovery of Nonlinear Functions: A Polynomial Sample Complexity Bound for Gaussian Random Fields Kefan Dong Tengyu Ma 43 4 0 29 Apr 2023
Augmented balancing weights as linear regression David Bruns-Smith O. Dukes Avi Feller Elizabeth L. Ogburn 27 10 0 27 Apr 2023
Kernel interpolation generalizes poorly Yicheng Li Haobo Zhang Qian Lin 42 11 0 28 Mar 2023
On the Connection between $L_p$ and Risk Consistency and its Implications on Regularized Kernel Methods Hannes Köhler 9 1 0 27 Mar 2023
On the Optimality of Misspecified Spectral Algorithms Hao Zhang Yicheng Li Qian Lin 23 15 0 27 Mar 2023
Sketching with Spherical Designs for Noisy Data Fitting on Spheres Shao-Bo Lin Di Wang Ding-Xuan Zhou 21 2 0 08 Mar 2023
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels T. Ahmad Luc Brogat-Motte Pierre Laforgue Florence dÁlché-Buc BDL 33 6 0 20 Feb 2023