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 Melanie Zeilinger 40 0 0 12 May 2025
Computation-Aware Kalman Filtering and Smoothing Marvin Pfortner Jonathan Wenger Jon Cockayne Philipp Hennig 91 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 50 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 42 1 0 18 Oct 2024
Embrace rejection: Kernel matrix approximation by accelerated randomly pivoted Cholesky Ethan N. Epperly J. Tropp R. Webber 39 4 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 48 5 0 30 Jun 2024
Nearest Neighbors GParareal: Improving Scalability of Gaussian Processes for Parallel-in-Time Solvers Guglielmo Gattiglio Lyudmila Grigoryeva M. Tamborrino 40 1 0 20 May 2024
Data-Driven Distributionally Robust Safety Verification Using Barrier Certificates and Conditional Mean Embeddings Oliver Schon Zhengang Zhong Sadegh Soudjani 39 8 0 15 Mar 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 31 0 0 31 Dec 2023
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds Paul Rosa Viacheslav Borovitskiy Alexander Terenin Judith Rousseau 39 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 47 10 0 05 Sep 2023
Nonlinear Meta-Learning Can Guarantee Faster Rates Dimitri Meunier Zhu Li Arthur Gretton Samory Kpotufe 35 6 0 20 Jul 2023
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension Moritz Haas David Holzmüller U. V. Luxburg Ingo Steinwart MLT 44 14 0 23 May 2023
Alignment of Density Maps in Wasserstein Distance A. Singer Ruiyi Yang 37 8 0 21 May 2023
Expressive Mortality Models through Gaussian Process Kernels M. Ludkovski Jimmy Risk 31 1 0 02 May 2023
Propagating Kernel Ambiguity Sets in Nonlinear Data-driven Dynamics Models Jia-Jie Zhu 31 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 37 5 0 11 Apr 2023
Reproducing kernel Hilbert spaces in the mean field limit Christian Fiedler Michael Herty M. Rom C. Segala Sebastian Trimpe 38 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 36 4 0 09 Feb 2023
Bandit Convex Optimisation Revisited: FTRL Achieves $\tilde{O}(t^{1/2})$ Regret David Young D. Leith Georgios Iosifidis 26 0 0 01 Feb 2023
(Private) Kernelized Bandits with Distributed Biased Feedback Fengjiao Li Xingyu Zhou Bo Ji 41 5 0 28 Jan 2023
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge S. Bouabid Jake Fawkes Dino Sejdinovic CML 49 0 0 26 Jan 2023
Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space Ayana Ghosh Sergei V. Kalinin M. Ziatdinov 27 8 0 06 Jan 2023
A Kernel Perspective of Skip Connections in Convolutional Networks Daniel Barzilai Amnon Geifman Meirav Galun Ronen Basri 28 12 0 27 Nov 2022
Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression Jiading Liu Lei Shi 33 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 21 0 0 19 Nov 2022
Isotropic Gaussian Processes on Finite Spaces of Graphs Viacheslav Borovitskiy Mohammad Reza Karimi Vignesh Ram Somnath Andreas Krause 40 7 0 03 Nov 2022
Optimisation & Generalisation in Networks of Neurons Jeremy Bernstein AI4CE 29 2 0 18 Oct 2022
Locally Smoothed Gaussian Process Regression Davit Gogolashvili B. Kozyrskiy Maurizio Filippone 30 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 63 7 0 14 Oct 2022
Bayesian Optimization with Informative Covariance Afonso Eduardo Michael U. Gutmann 29 3 0 04 Aug 2022
Optimal Rates for Regularized Conditional Mean Embedding Learning Zhu Li Dimitri Meunier Mattes Mollenhauer Arthur Gretton 40 47 0 02 Aug 2022
Collaborative Learning in Kernel-based Bandits for Distributed Users Sudeep Salgia Sattar Vakili Qing Zhao FedML 44 6 0 16 Jul 2022
Learning nonparametric ordinary differential equations from noisy data Kamel Lahouel Michael L. Wells Victor Rielly Ethan Lew David M Lovitz Bruno Jedynak 34 5 0 30 Jun 2022
Posterior and Computational Uncertainty in Gaussian Processes Jonathan Wenger Geoff Pleiss Marvin Pfortner Philipp Hennig John P. Cunningham 85 19 0 30 May 2022
Fast Instrument Learning with Faster Rates Ziyu Wang Yuhao Zhou Jun Zhu 36 3 0 22 May 2022
Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization Shouri Hu Haowei Wang Zhongxiang Dai K. H. Low Szu Hui Ng 33 4 0 10 May 2022
Instance-Dependent Regret Analysis of Kernelized Bandits S. Shekhar T. Javidi 29 3 0 12 Mar 2022
Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation Toni Karvonen 32 1 0 10 Mar 2022
AutoIP: A United Framework to Integrate Physics into Gaussian Processes D. Long Junyao Xing Aditi S. Krishnapriyan Robert M. Kirby Shandian Zhe Michael W. Mahoney AI4CE 39 14 0 24 Feb 2022
Data-Driven Chance Constrained Control using Kernel Distribution Embeddings Adam J. Thorpe T. Lew Meeko Oishi Marco Pavone 38 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 38 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 31 3 0 19 Nov 2021
Misspecified Gaussian Process Bandit Optimization Ilija Bogunovic Andreas Krause 57 43 0 09 Nov 2021
Uniform Generalization Bounds for Overparameterized Neural Networks Sattar Vakili Michael Bromberg Jezabel R. Garcia Da-shan Shiu A. Bernacchia 35 19 0 13 Sep 2021
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications A. Capone Armin Lederer Sandra Hirche 32 18 0 06 Sep 2021