Efficiently Sampling Functions from Gaussian Process Posteriors

21 February 2020

James T. Wilson

Viacheslav Borovitskiy

Papers citing "Efficiently Sampling Functions from Gaussian Process Posteriors"

44 / 44 papers shown

Title
Computation-Aware Kalman Filtering and Smoothing Marvin Pfortner Jonathan Wenger Jon Cockayne Philipp Hennig 86 3 0 13 Mar 2025
Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies from Simulated Nonparametric Functions Cen-You Li Marc Toussaint Barbara Rakitsch Christoph Zimmer OffRL 155 0 0 26 Jan 2025
Residual Deep Gaussian Processes on Manifolds Kacper Wyrwal Andreas Krause Viacheslav Borovitskiy BDL 49 0 0 31 Oct 2024
Optimizing Posterior Samples for Bayesian Optimization via Rootfinding Taiwo A. Adebiyi Bach Do Ruda Zhang 114 2 0 29 Oct 2024
Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure Jihao Andreas Lin Sebastian Ament Maximilian Balandat E. Bakshy BDL 29 2 0 11 Oct 2024
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes J. Lin Shreyas Padhy Bruno Mlodozeniec Javier Antorán José Miguel Hernández-Lobato 43 2 0 28 May 2024
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent Yingru Li Jiawei Xu Lei Han Zhi-Quan Luo BDL OffRL 26 6 0 05 Feb 2024
Practical Path-based Bayesian Optimization Jose Pablo Folch J. Odgers Shiqiang Zhang Robert M. Lee B. Shafei David Walz Calvin Tsay Mark van der Wilk Ruth Misener 44 2 0 01 Dec 2023
Implicit Manifold Gaussian Process Regression Bernardo Fichera Viacheslav Borovitskiy Andreas Krause A. Billard 18 3 0 30 Oct 2023
Exact Inference for Continuous-Time Gaussian Process Dynamics K. Ensinger Nicholas Tagliapietra Sebastian Ziesche Sebastian Trimpe 26 1 0 05 Sep 2023
Learning Switching Port-Hamiltonian Systems with Uncertainty Quantification Thomas Beckers Tom Z. Jiahao George J. Pappas 31 2 0 15 May 2023
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior Thomas Beckers Jacob H. Seidman P. Perdikaris George J. Pappas PINN 29 17 0 15 May 2023
Mastering the exploration-exploitation trade-off in Bayesian Optimization Antonio Candelieri 26 1 0 15 May 2023
MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning Adam X. Yang Laurence Aitchison Henry B. Moss 29 4 0 22 Feb 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
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation Henry B. Moss Sebastian W. Ober Victor Picheny 32 24 0 24 Jan 2023
The Past Does Matter: Correlation of Subsequent States in Trajectory Predictions of Gaussian Process Models Steffen Ridderbusch Sina Ober-Blobaum Paul Goulart 11 2 0 20 Nov 2022
Provably Reliable Large-Scale Sampling from Gaussian Processes Anthony Stephenson Robert Allison Edward O. Pyzer-Knapp 21 2 0 15 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
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
Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processes Joe Watson Jan Peters 26 15 0 07 Oct 2022
Joint Entropy Search for Multi-objective Bayesian Optimization Ben Tu Axel Gandy N. Kantas B. Shafei 21 38 0 06 Oct 2022
Sample-based Uncertainty Quantification with a Single Deterministic Neural Network T. Kanazawa Chetan Gupta UQCV 30 4 0 17 Sep 2022
Variational Inference for Model-Free and Model-Based Reinforcement Learning Felix Leibfried OffRL 13 0 0 04 Sep 2022
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case I. Azangulov A. Smolensky Alexander Terenin Viacheslav Borovitskiy 39 21 0 31 Aug 2022
Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation Henry B. Moss Sebastian W. Ober Victor Picheny 20 4 0 06 Jun 2022
Gamifying optimization: a Wasserstein distance-based analysis of human search Antonio Candelieri Andrea Ponti F. Archetti 13 0 0 12 Dec 2021
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels Noémie Jaquier Viacheslav Borovitskiy A. Smolensky Alexander Terenin Tamim Asfour Leonel Rozo 26 35 0 02 Nov 2021
Efficient Exploration in Binary and Preferential Bayesian Optimization T. Fauvel M. Chalk 19 7 0 18 Oct 2021
Uncertainty Quantification and Experimental Design for Large-Scale Linear Inverse Problems under Gaussian Process Priors Cédric Travelletti D. Ginsbourger N. Linde 16 3 0 08 Sep 2021
Bayesian Optimization with High-Dimensional Outputs Wesley J. Maddox Maximilian Balandat A. Wilson E. Bakshy UQCV 24 50 0 24 Jun 2021
Variational multiple shooting for Bayesian ODEs with Gaussian processes Pashupati Hegde Çağatay Yıldız Harri Lähdesmäki Samuel Kaski Markus Heinonen 22 16 0 21 Jun 2021
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information W. Neiswanger Ke Alexander Wang Stefano Ermon MLAU 29 30 0 19 Apr 2021
Uncertainty quantification and exploration-exploitation trade-off in humans Antonio Candelieri Andrea Ponti F. Archetti 21 4 0 05 Feb 2021
A Tutorial on Sparse Gaussian Processes and Variational Inference Felix Leibfried Vincent Dutordoir S. T. John N. Durrande GP 42 49 0 27 Dec 2020
Cluster-Specific Predictions with Multi-Task Gaussian Processes Arthur Leroy Pierre Latouche Benjamin Guedj S. Gey 11 4 0 16 Nov 2020
Pathwise Conditioning of Gaussian Processes James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 18 57 0 08 Nov 2020
Matérn Gaussian Processes on Graphs Viacheslav Borovitskiy I. Azangulov Alexander Terenin P. Mostowsky M. Deisenroth N. Durrande 13 78 0 29 Oct 2020
Asynchronous ε-Greedy Bayesian Optimisation George De Ath Richard Everson J. Fieldsend 35 5 0 15 Oct 2020
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization Geoff Pleiss M. Jankowiak David Eriksson Anil Damle Jacob R. Gardner 17 43 0 19 Jun 2020
Matérn Gaussian processes on Riemannian manifolds Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 9 120 0 17 Jun 2020
Scalable Thompson Sampling using Sparse Gaussian Process Models Sattar Vakili Henry B. Moss A. Artemev Vincent Dutordoir Victor Picheny 13 34 0 09 Jun 2020
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation Ryan-Rhys Griffiths Alexander A. Aldrick Miguel García-Ortegón Vidhi R. Lalchand A. Lee 31 35 0 17 Oct 2019
Gaussian Processes with Errors in Variables: Theory and Computation Shuang Zhou D. Pati Tianying Wang Yun Yang R. Carroll 16 3 0 14 Oct 2019