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Approximation beats concentration? An approximation view on inference with smooth radial kernels

10 January 2018

Papers citing "Approximation beats concentration? An approximation view on inference with smooth radial kernels"

50 / 63 papers shown

Title
A general technique for approximating high-dimensional empirical kernel matrices Chiraag Kaushik Justin Romberg Vidya Muthukumar 54 0 0 05 Nov 2025
Bayesian Optimization over Bounded Domains with the Beta Product KernelConference on Uncertainty in Artificial Intelligence (UAI), 2025 Huy Hoang Nguyen Han Zhou Matthew B. Blaschko A. Tiulpin 125 0 0 19 Jun 2025
Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project Pratik Rathore Zachary Frangella Sachin Garg Shaghayegh Fazliani Michał Dereziński Madeleine Udell 253 2 0 19 May 2025
Sparse Nonparametric Contextual Bandits Hamish Flynn Julia Olkhovskaya Paul Rognon-Vael 291 0 0 20 Mar 2025
Koopman-Equivariant Gaussian ProcessesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2025 Nicolas Hoischen Max Beier Armin Lederer A. Capone Roland Toth Sandra Hirche AI4TS 250 5 0 10 Feb 2025
Fast Second-Order Online Kernel Learning through Incremental Matrix Sketching and DecompositionInternational Joint Conference on Artificial Intelligence (IJCAI), 2024 Dongxie Wen Xiao Zhang Zhewei Wei Chenping Hou Shuai Li Weinan Zhang 169 0 0 15 Oct 2024
Have ASkotch: A Neat Solution for Large-scale Kernel Ridge Regression Pratik Rathore Zachary Frangella Madeleine Udell Michał Dereziński Madeleine Udell 283 4 0 14 Jul 2024
Regularized KL-Divergence for Well-Defined Function-Space Variational Inference in Bayesian neural networks Tristan Cinquin Kushagra Pandey UQCV BDL 369 2 0 06 Jun 2024
Entrywise error bounds for low-rank approximations of kernel matricesNeural Information Processing Systems (NeurIPS), 2024 Alexander Modell 234 0 0 23 May 2024
Tighter Confidence Bounds for Sequential Kernel Regression H. Flynn David Reeb 192 5 0 19 Mar 2024
A Bound on the Maximal Marginal Degrees of Freedom Paul Dommel 260 1 0 20 Feb 2024
Generalization in Kernel Regression Under Realistic Assumptions Daniel Barzilai Ohad Shamir 315 21 0 26 Dec 2023
Regret Optimality of GP-UCB Wenjia Wang Xiaowei Zhang Lu Zou 184 2 0 03 Dec 2023
Spatial Process Approximations: Assessing Their Necessity Hao Zhang 81 2 0 06 Nov 2023
An Improved Uniform Convergence Bound with Fat-Shattering DimensionInformation Processing Letters (IPL), 2023 Roberto Colomboni Emmanuel Esposito Andrea Paudice 86 2 0 13 Jul 2023
Stochastic Nonlinear Control via Finite-dimensional Spectral Dynamic EmbeddingIEEE Conference on Decision and Control (CDC), 2023 Zhaolin Ren Tongzheng Ren Haitong Ma Na Li Bo Dai 253 1 0 08 Apr 2023
Interpolation with the polynomial kernels G. Elefante W. Erb Francesco Marchetti E. Perracchione D. Poggiali G. Santin 159 1 0 15 Dec 2022
Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning ModelsAAAI Conference on Artificial Intelligence (AAAI), 2022 Qiong Wu Jian Li Zhenming Liu Jun Luo Mihai Cucuringu 145 5 0 01 Dec 2022
Provably Reliable Large-Scale Sampling from Gaussian Processes Anthony Stephenson Robert Allison Edward O. Pyzer-Knapp 173 2 0 15 Nov 2022
Optimal plug-in Gaussian processes for modelling derivatives Zejian Liu Meng Li 206 6 0 20 Oct 2022
A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networksNeural Information Processing Systems (NeurIPS), 2022 El Mehdi Achour Armand Foucault Sébastien Gerchinovitz Franccois Malgouyres 187 10 0 09 Jun 2022
Rigorous data-driven computation of spectral properties of Koopman operators for dynamical systems Matthew J. Colbrook Alex Townsend 289 94 0 29 Nov 2021
Generalization Performance of Empirical Risk Minimization on Over-parameterized Deep ReLU NetsIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2021 Shao-Bo Lin Yao Wang Ding-Xuan Zhou ODL 284 5 0 28 Nov 2021
Harmless interpolation in regression and classification with structured featuresInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2021 Andrew D. McRae Santhosh Karnik Mark A. Davenport Vidya Muthukumar 286 13 0 09 Nov 2021
Dynamic Pricing and Demand Learning on a Large Network of Products: A PAC-Bayesian Approach Bora Keskin D. Simchi-Levi Prem M. Talwai 203 1 0 01 Nov 2021
Learning to Forecast Dynamical Systems from Streaming Data D. Giannakis Amelia Henriksen J. Tropp Rachel A. Ward AI4TS 177 20 0 20 Sep 2021
Learning Partial Differential Equations in Reproducing Kernel Hilbert SpacesJournal of machine learning research (JMLR), 2021 George Stepaniants 206 22 0 26 Aug 2021
Neural Contextual Bandits without Regret Parnian Kassraie Andreas Krause OffRL 241 42 0 07 Jul 2021
Weighted Gaussian Process Bandits for Non-stationary Environments Yuntian Deng Xingyu Zhou Baekjin Kim Ambuj Tewari Abhishek Gupta Ness B. Shroff 204 29 0 06 Jul 2021
Shallow Representation is Deep: Learning Uncertainty-aware and Worst-case Random Feature Dynamics Diego Agudelo-España Yassine Nemmour Bernhard Schölkopf Jia-Jie Zhu OOD BDL 86 0 0 24 Jun 2021
Fast Statistical Leverage Score Approximation in Kernel Ridge RegressionInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2021 Yifan Chen Yun Yang 116 17 0 09 Mar 2021
Adversarially Robust Kernel SmoothingInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2021 Jia-Jie Zhu Christina Kouridi Yassine Nemmour Bernhard Schölkopf 202 8 0 16 Feb 2021
On Information Gain and Regret Bounds in Gaussian Process BanditsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020 Sattar Vakili Kia Khezeli Victor Picheny GP 423 154 0 15 Sep 2020
Kernel Interpolation of High Dimensional Scattered DataSIAM Journal on Numerical Analysis (SINUM), 2020 Shao-Bo Lin Xiangyu Chang Xingping Sun 216 10 0 03 Sep 2020
Canonical thresholding for non-sparse high-dimensional linear regressionAnnals of Statistics (Ann. Stat.), 2020 I. Silin Jianqing Fan 130 6 0 24 Jul 2020
Spectral Bias and Task-Model Alignment Explain Generalization in Kernel Regression and Infinitely Wide Neural NetworksNature Communications (Nat Commun), 2020 Abdulkadir Canatar Blake Bordelon Cengiz Pehlevan 499 219 0 23 Jun 2020
Interpolation and Learning with Scale Dependent Kernels Nicolò Pagliana Alessandro Rudi Ernesto De Vito Lorenzo Rosasco 287 8 0 17 Jun 2020
Scalable Thompson Sampling using Sparse Gaussian Process Models Sattar Vakili Henry B. Moss A. Artemev Vincent Dutordoir Victor Picheny 360 35 0 09 Jun 2020
Lower bounds for invariant statistical models with applications to principal component analysis Martin Wahl 127 6 0 14 May 2020
Analyzing the discrepancy principle for kernelized spectral filter learning algorithmsJournal of machine learning research (JMLR), 2020 Alain Celisse Martin Wahl 132 19 0 17 Apr 2020
Kernel Autocovariance Operators of Stationary Processes: Estimation and ConvergenceJournal of machine learning research (JMLR), 2020 Mattes Mollenhauer Stefan Klus Christof Schütte P. Koltai 183 11 0 02 Apr 2020
Sample Complexity Result for Multi-category Classifiers of Bounded Variation Khadija Musayeva 176 2 0 20 Mar 2020
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and SmoothnessJournal of machine learning research (JMLR), 2020 George Wynne F. Briol Mark Girolami 318 67 0 29 Jan 2020
Towards Understanding the Spectral Bias of Deep LearningInternational Joint Conference on Artificial Intelligence (IJCAI), 2019 Yuan Cao Zhiying Fang Yue Wu Ding-Xuan Zhou Quanquan Gu 327 254 0 03 Dec 2019
On the perturbation series for eigenvalues and eigenprojections Martin Wahl 102 10 0 18 Oct 2019
High-probability bounds for the reconstruction error of PCAStatistics and Probability Letters (Stat. Probab. Lett.), 2019 Cassandra Milbradt Martin Wahl 147 10 0 24 Sep 2019
On identifiability and consistency of the nugget in Gaussian spatial process models Wenpin Tang Lu Zhang Sudipto Banerjee 384 42 0 15 Aug 2019
Benign Overfitting in Linear RegressionProceedings of the National Academy of Sciences of the United States of America (PNAS), 2019 Peter L. Bartlett Philip M. Long Gábor Lugosi Alexander Tsigler MLT 353 844 0 26 Jun 2019
Nyström landmark sampling and regularized Christoffel functionsMachine-mediated learning (ML), 2019 Michaël Fanuel J. Schreurs Johan A. K. Suykens 244 14 0 29 May 2019
Analyzing Data Selection Techniques with Tools from the Theory of Information Losses Brandon Foggo N. Yu 166 2 0 25 Feb 2019