Understanding Probabilistic Sparse Gaussian Process Approximations

15 June 2016

Papers citing "Understanding Probabilistic Sparse Gaussian Process Approximations"

45 / 45 papers shown

Title
Sparse Gaussian Neural Processes Tommy Rochussen Vincent Fortuin BDL UQCV 66 0 0 02 Apr 2025
MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning Peter Eckmann D. Wu G. Heinzelmann Michael K. Gilson Rose Yu AI4CE 49 0 0 15 Oct 2024
Noise-Aware Differentially Private Regression via Meta-Learning Ossi Raisa Stratis Markou Matthew Ashman W. Bruinsma Marlon Tobaben Antti Honkela Richard Turner 82 1 0 12 Jun 2024
Robust Inference of Dynamic Covariance Using Wishart Processes and Sequential Monte Carlo Hester Huijsdens D. Leeftink Linda Geerligs Max Hinne 37 0 0 07 Jun 2024
Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes (with Appendix) Kalvik Jakkala Srinivas Akella 27 8 0 13 Sep 2023
Multi-Response Heteroscedastic Gaussian Process Models and Their Inference Taehee Lee Jun S. Liu 13 1 0 29 Aug 2023
GP-Frontier for Local Mapless Navigation Mahmoud Ali Lantao Liu 13 11 0 21 Jul 2023
Free-Form Variational Inference for Gaussian Process State-Space Models Xuhui Fan Edwin V. Bonilla T. O’Kane Scott A. Sisson 16 9 0 20 Feb 2023
Memory Safe Computations with XLA Compiler A. Artemev Tilman Roeder Mark van der Wilk 37 8 0 28 Jun 2022
Posterior and Computational Uncertainty in Gaussian Processes Jonathan Wenger Geoff Pleiss Marvin Pfortner Philipp Hennig John P. Cunningham 80 19 0 30 May 2022
Bridging the reality gap in quantum devices with physics-aware machine learning D. L. Craig H. Moon F. Fedele D. Lennon B. V. Straaten ... D. Zumbuhl G. Briggs Michael A. Osborne D. Sejdinovic N. Ares 28 13 0 22 Nov 2021
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits Hao Chen Lili Zheng Raed Al Kontar Garvesh Raskutti 24 3 0 19 Nov 2021
Uncertainty quantification of a three-dimensional in-stent restenosis model with surrogate modelling Dongwei Ye Pavel S. Zun Valeria Krzhizhanovskaya Alfons G. Hoekstra 27 1 0 11 Nov 2021
Spatio-Temporal Variational Gaussian Processes Oliver Hamelijnck William J. Wilkinson Niki A. Loppi Arno Solin Theodoros Damoulas AI4TS 21 31 0 02 Nov 2021
Distributed Gaussian Process Mapping for Robot Teams with Time-varying Communication James Di Ehsan Zobeidi Alec Koppel Nikolay Atanasov 28 2 0 12 Oct 2021
Adaptive Inducing Points Selection For Gaussian Processes Théo Galy-Fajou Manfred Opper 31 15 0 21 Jul 2021
Meta-Learning Reliable Priors in the Function Space Jonas Rothfuss Dominique Heyn Jinfan Chen Andreas Krause 42 27 0 06 Jun 2021
Scalable Cross Validation Losses for Gaussian Process Models M. Jankowiak Geoff Pleiss 28 6 0 24 May 2021
Learning-enhanced robust controller synthesis with rigorous statistical and control-theoretic guarantees Christian Fiedler C. Scherer Sebastian Trimpe 24 15 0 07 May 2021
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion D. Svendsen Pablo Morales-Álvarez A. Ruescas Rafael Molina Gustau Camps-Valls 30 29 0 16 Apr 2021
How Powerful are Performance Predictors in Neural Architecture Search? Colin White Arber Zela Binxin Ru Yang Liu Frank Hutter 22 126 0 02 Apr 2021
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations Luhuan Wu Andrew C. Miller Lauren Anderson Geoff Pleiss David M. Blei John P. Cunningham 27 8 0 28 Feb 2021
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients A. Artemev David R. Burt Mark van der Wilk 23 18 0 16 Feb 2021
Bias-Free Scalable Gaussian Processes via Randomized Truncations Andres Potapczynski Luhuan Wu D. Biderman Geoff Pleiss John P. Cunningham 26 19 0 12 Feb 2021
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes A. Benavoli Dario Azzimonti Dario Piga 29 15 0 12 Dec 2020
Cluster-Specific Predictions with Multi-Task Gaussian Processes Arthur Leroy Pierre Latouche Benjamin Guedj S. Gey 23 4 0 16 Nov 2020
Learning ODE Models with Qualitative Structure Using Gaussian Processes Steffen Ridderbusch Christian Offen Sina Ober-Blobaum Paul Goulart 10 15 0 10 Nov 2020
Transforming Gaussian Processes With Normalizing Flows Juan Maroñas Oliver Hamelijnck Jeremias Knoblauch Theodoros Damoulas 34 34 0 03 Nov 2020
Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling Nishant Yadav S. Ravela A. Ganguly OOD AI4Cl AI4CE 22 3 0 12 Aug 2020
Convergence of Sparse Variational Inference in Gaussian Processes Regression David R. Burt C. Rasmussen Mark van der Wilk 29 69 0 01 Aug 2020
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization Geoff Pleiss M. Jankowiak David Eriksson Anil Damle Jacob R. Gardner 19 43 0 19 Jun 2020
Skew Gaussian Processes for Classification A. Benavoli Dario Azzimonti Dario Piga GP 32 19 0 26 May 2020
Variational Inference with Vine Copulas: An efficient Approach for Bayesian Computer Model Calibration Vojtech Kejzlar T. Maiti 16 6 0 28 Mar 2020
A Framework for Interdomain and Multioutput Gaussian Processes Mark van der Wilk Vincent Dutordoir S. T. John A. Artemev Vincent Adam J. Hensman 42 94 0 02 Mar 2020
Doubly Sparse Variational Gaussian Processes Vincent Adam Stefanos Eleftheriadis N. Durrande A. Artemev J. Hensman 27 24 0 15 Jan 2020
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO Pablo Morales-Álvarez Pablo Ruiz S. Coughlin Rafael Molina Aggelos K. Katsaggelos 21 14 0 05 Nov 2019
Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics Iñigo Urteaga Tristan Bertin Theresa M. Hardy D. Albers Noémie Elhadad AI4TS AI4CE 16 5 0 27 Aug 2019
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes Creighton Heaukulani Mark van der Wilk BDL 32 15 0 22 Jun 2019
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models Alessandro Davide Ialongo Mark van der Wilk J. Hensman C. Rasmussen 44 30 0 13 Jun 2019
Rates of Convergence for Sparse Variational Gaussian Process Regression David R. Burt C. Rasmussen Mark van der Wilk 26 151 0 08 Mar 2019
Learning Invariances using the Marginal Likelihood Mark van der Wilk Matthias Bauer S. T. John J. Hensman 36 83 0 16 Aug 2018
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees Jonathan H. Huggins Trevor Campbell Mikolaj Kasprzak Tamara Broderick 35 15 0 26 Jun 2018
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression Haitao Liu Jianfei Cai Yi Wang Yew-Soon Ong 26 83 0 03 Jun 2018
Practical Transfer Learning for Bayesian Optimization Matthias Feurer Benjamin Letham Frank Hutter E. Bakshy 55 34 0 06 Feb 2018
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation T. Bui Josiah Yan Richard Turner 24 25 0 23 May 2016