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1801.03437
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Approximation beats concentration? An approximation view on inference with smooth radial kernels
10 January 2018
M. Belkin
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Papers citing
"Approximation beats concentration? An approximation view on inference with smooth radial kernels"
50 / 63 papers shown
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Koopman-Equivariant Gaussian Processes
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Fast Second-Order Online Kernel Learning through Incremental Matrix Sketching and Decomposition
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14 Jul 2024
Regularized KL-Divergence for Well-Defined Function-Space Variational Inference in Bayesian neural networks
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Entrywise error bounds for low-rank approximations of kernel matrices
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Tighter Confidence Bounds for Sequential Kernel Regression
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211
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191
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207
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Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces
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234
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285
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Weighted Gaussian Process Bandits for Non-stationary Environments
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Xingyu Zhou
Baekjin Kim
Ambuj Tewari
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260
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Shallow Representation is Deep: Learning Uncertainty-aware and Worst-case Random Feature Dynamics
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Yassine Nemmour
Bernhard Schölkopf
Jia-Jie Zhu
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94
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24 Jun 2021
Fast Statistical Leverage Score Approximation in Kernel Ridge Regression
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Yifan Chen
Yun Yang
156
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09 Mar 2021
Adversarially Robust Kernel Smoothing
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Jia-Jie Zhu
Christina Kouridi
Yassine Nemmour
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266
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16 Feb 2021
On Information Gain and Regret Bounds in Gaussian Process Bandits
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Sattar Vakili
Kia Khezeli
Victor Picheny
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476
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15 Sep 2020
Kernel Interpolation of High Dimensional Scattered Data
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Xiangyu Chang
Xingping Sun
232
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Canonical thresholding for non-sparse high-dimensional linear regression
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I. Silin
Jianqing Fan
166
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Spectral Bias and Task-Model Alignment Explain Generalization in Kernel Regression and Infinitely Wide Neural Networks
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Abdulkadir Canatar
Blake Bordelon
Cengiz Pehlevan
567
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Interpolation and Learning with Scale Dependent Kernels
Nicolò Pagliana
Alessandro Rudi
Ernesto De Vito
Lorenzo Rosasco
291
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Scalable Thompson Sampling using Sparse Gaussian Process Models
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Henry B. Moss
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Vincent Dutordoir
Victor Picheny
392
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Lower bounds for invariant statistical models with applications to principal component analysis
Martin Wahl
163
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Analyzing the discrepancy principle for kernelized spectral filter learning algorithms
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Alain Celisse
Martin Wahl
184
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Kernel Autocovariance Operators of Stationary Processes: Estimation and Convergence
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Mattes Mollenhauer
Stefan Klus
Christof Schütte
P. Koltai
219
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02 Apr 2020
Sample Complexity Result for Multi-category Classifiers of Bounded Variation
Khadija Musayeva
188
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20 Mar 2020
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness
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George Wynne
F. Briol
Mark Girolami
378
68
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29 Jan 2020
Towards Understanding the Spectral Bias of Deep Learning
International Joint Conference on Artificial Intelligence (IJCAI), 2019
Yuan Cao
Zhiying Fang
Yue Wu
Ding-Xuan Zhou
Quanquan Gu
455
266
0
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On the perturbation series for eigenvalues and eigenprojections
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154
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High-probability bounds for the reconstruction error of PCA
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151
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Lu Zhang
Sudipto Banerjee
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Benign Overfitting in Linear Regression
Proceedings of the National Academy of Sciences of the United States of America (PNAS), 2019
Peter L. Bartlett
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Gábor Lugosi
Alexander Tsigler
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405
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26 Jun 2019
Nyström landmark sampling and regularized Christoffel functions
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284
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N. Yu
178
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