Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences

6 July 2018

Motonobu Kanagawa

Philipp Hennig

Dino Sejdinovic

Bharath K. Sriperumbudur

BDL

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Papers citing "Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences"

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Title
Finite-Sample-Based Reachability for Safe Control with Gaussian Process Dynamics Manish Prajapat Johannes Köhler Amon Lahr Andreas Krause M. Zeilinger 38 0 0 12 May 2025
Computation-Aware Kalman Filtering and Smoothing Marvin Pfortner Jonathan Wenger Jon Cockayne Philipp Hennig 86 3 0 13 Mar 2025
Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis Yasamin Jalalian Juan Felipe Osorio Ramirez Alexander W. Hsu Bamdad Hosseini H. Owhadi 43 0 0 02 Mar 2025
Optimizing Posterior Samples for Bayesian Optimization via Rootfinding Taiwo A. Adebiyi Bach Do Ruda Zhang 114 2 0 29 Oct 2024
Spectral Representations for Accurate Causal Uncertainty Quantification with Gaussian Processes Hugh Dance Peter Orbanz Arthur Gretton CML 34 1 0 18 Oct 2024
Embrace rejection: Kernel matrix approximation by accelerated randomly pivoted Cholesky Ethan N. Epperly J. Tropp R. Webber 34 3 0 04 Oct 2024
Meta-Analysis with Untrusted Data Shiva Kaul Geoffrey J. Gordon CML 32 1 0 12 Jul 2024
Hyperparameter Optimization for Randomized Algorithms: A Case Study on Random Features Oliver R. A. Dunbar Nicholas H. Nelsen Maya Mutic 30 5 0 30 Jun 2024
Nearest Neighbors GParareal: Improving Scalability of Gaussian Processes for Parallel-in-Time Solvers Guglielmo Gattiglio Lyudmila Grigoryeva M. Tamborrino 32 1 0 20 May 2024
Global Safe Sequential Learning via Efficient Knowledge Transfer Cen-You Li Olaf Duennbier Marc Toussaint Barbara Rakitsch Christoph Zimmer 52 2 0 22 Feb 2024
Smoothness Estimation for Whittle-Matérn Processes on Closed Riemannian Manifolds Moritz Korte-Stapff Toni Karvonen Eric Moulines 23 0 0 31 Dec 2023
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds Paul Rosa Viacheslav Borovitskiy Alexander Terenin Judith Rousseau 30 7 0 19 Sep 2023
Distributionally Robust Model-based Reinforcement Learning with Large State Spaces Shyam Sundhar Ramesh Pier Giuseppe Sessa Yifan Hu Andreas Krause Ilija Bogunovic OOD 42 10 0 05 Sep 2023
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension Moritz Haas David Holzmüller U. V. Luxburg Ingo Steinwart MLT 35 14 0 23 May 2023
Alignment of Density Maps in Wasserstein Distance A. Singer Ruiyi Yang 25 8 0 21 May 2023
Propagating Kernel Ambiguity Sets in Nonlinear Data-driven Dynamics Models Jia-Jie Zhu 21 0 0 27 Apr 2023
Actually Sparse Variational Gaussian Processes Harry Jake Cunningham Daniel Augusto R. M. A. de Souza So Takao Mark van der Wilk M. Deisenroth 29 5 0 11 Apr 2023
Reproducing kernel Hilbert spaces in the mean field limit Christian Fiedler Michael Herty M. Rom C. Segala Sebastian Trimpe 27 6 0 28 Feb 2023
Introduction To Gaussian Process Regression In Bayesian Inverse Problems, With New ResultsOn Experimental Design For Weighted Error Measures T. Helin Andrew M. Stuart A. Teckentrup K. Zygalakis 34 4 0 09 Feb 2023
Bandit Convex Optimisation Revisited: FTRL Achieves $\tilde{O}(t^{1/2})$ Regret David Young D. Leith Georgios Iosifidis 13 0 0 01 Feb 2023
(Private) Kernelized Bandits with Distributed Biased Feedback Fengjiao Li Xingyu Zhou Bo Ji 33 5 0 28 Jan 2023
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge S. Bouabid Jake Fawkes Dino Sejdinovic CML 44 0 0 26 Jan 2023
Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression Jiading Liu Lei Shi 17 9 0 20 Nov 2022
On the Multidimensional Augmentation of Fingerprint Data for Indoor Localization in A Large-Scale Building Complex Based on Multi-Output Gaussian Process Zhe Tang Sihao Li Kyeong Soo Kim Jeremy Smith 11 0 0 19 Nov 2022
Isotropic Gaussian Processes on Finite Spaces of Graphs Viacheslav Borovitskiy Mohammad Reza Karimi Vignesh Ram Somnath Andreas Krause 35 7 0 03 Nov 2022
Optimisation & Generalisation in Networks of Neurons Jeremy Bernstein AI4CE 24 2 0 18 Oct 2022
Locally Smoothed Gaussian Process Regression Davit Gogolashvili B. Kozyrskiy Maurizio Filippone 16 8 0 18 Oct 2022
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees Alexander Terenin David R. Burt A. Artemev Seth Flaxman Mark van der Wilk C. Rasmussen Hong Ge 58 7 0 14 Oct 2022
Bayesian Optimization with Informative Covariance Afonso Eduardo Michael U. Gutmann 24 3 0 04 Aug 2022
Optimal Rates for Regularized Conditional Mean Embedding Learning Zhu Li D. Meunier Mattes Mollenhauer A. Gretton 30 46 0 02 Aug 2022
Collaborative Learning in Kernel-based Bandits for Distributed Users Sudeep Salgia Sattar Vakili Qing Zhao FedML 37 6 0 16 Jul 2022
Learning nonparametric ordinary differential equations from noisy data Kamel Lahouel Michael Wells Victor Rielly Ethan Lew David M Lovitz Bruno Michel Jedynak 26 5 0 30 Jun 2022
Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization Shouri Hu Haowei Wang Zhongxiang Dai K. H. Low Szu Hui Ng 17 4 0 10 May 2022
Instance-Dependent Regret Analysis of Kernelized Bandits S. Shekhar T. Javidi 13 3 0 12 Mar 2022
Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation Toni Karvonen 21 1 0 10 Mar 2022
AutoIP: A United Framework to Integrate Physics into Gaussian Processes D. Long Zhilin Wang Aditi S. Krishnapriyan Robert M. Kirby Shandian Zhe Michael W. Mahoney AI4CE 26 14 0 24 Feb 2022
Data-Driven Chance Constrained Control using Kernel Distribution Embeddings Adam J. Thorpe T. Lew Meeko Oishi Marco Pavone 25 21 0 08 Feb 2022
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning Sattar Vakili Jonathan Scarlett Da-shan Shiu A. Bernacchia 25 18 0 08 Feb 2022
GParareal: A time-parallel ODE solver using Gaussian process emulation K. Pentland M. Tamborrino Timothy John Sullivan J. Buchanan Lynton C. Appel 11 8 0 31 Jan 2022
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits Hao Chen Lili Zheng Raed Al Kontar Garvesh Raskutti 20 3 0 19 Nov 2021
Misspecified Gaussian Process Bandit Optimization Ilija Bogunovic Andreas Krause 53 42 0 09 Nov 2021
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications A. Capone Armin Lederer Sandra Hirche 24 18 0 06 Sep 2021
Optimal Order Simple Regret for Gaussian Process Bandits Sattar Vakili N. Bouziani Sepehr Jalali A. Bernacchia Da-shan Shiu 31 51 0 20 Aug 2021
Discrepancy-based Inference for Intractable Generative Models using Quasi-Monte Carlo Ziang Niu J. Meier F. Briol 26 12 0 22 Jun 2021
Locality defeats the curse of dimensionality in convolutional teacher-student scenarios Alessandro Favero Francesco Cagnetta M. Wyart 30 31 0 16 Jun 2021
Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression Christian Fiedler C. Scherer Sebastian Trimpe GP 26 65 0 06 May 2021
Bayesian Numerical Methods for Nonlinear Partial Differential Equations Junyang Wang Jon Cockayne O. Chkrebtii T. Sullivan Chris J. Oates 53 19 0 22 Apr 2021
Small Sample Spaces for Gaussian Processes Toni Karvonen 14 13 0 04 Mar 2021
Pathwise Conditioning of Gaussian Processes James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 18 57 0 08 Nov 2020
Stable ResNet Soufiane Hayou Eugenio Clerico Bo He George Deligiannidis Arnaud Doucet Judith Rousseau ODL SSeg 46 51 0 24 Oct 2020