Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent

20 June 2023

J. Lin

Javier Antorán

Shreyas Padhy

David Janz

José Miguel Hernández-Lobato

Alexander Terenin

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Papers citing "Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent"

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Title
Stochastic Poisson Surface Reconstruction with One Solve using Geometric Gaussian Processes Sidhanth Holalkere David S. Bindel Silvia Sellán Alexander Terenin 56 0 0 24 Mar 2025
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
Towards Gaussian Process for operator learning: an uncertainty aware resolution independent operator learning algorithm for computational mechanics Sawan Kumar R. Nayek Souvik Chakraborty 33 1 0 17 Sep 2024
Towards safe and tractable Gaussian process-based MPC: Efficient sampling within a sequential quadratic programming framework Manish Prajapat Amon Lahr Johannes Köhler Andreas Krause Melanie Zeilinger 26 2 0 13 Sep 2024
Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matrices Frida Viset Anton Kullberg Frederiek Wesel Arno Solin 32 0 0 05 Aug 2024
Gaussian Processes Sampling with Sparse Grids under Additive Schwarz Preconditioner Haoyuan Chen Rui Tuo 37 0 0 01 Aug 2024
Have ASkotch: A Neat Solution for Large-scale Kernel Ridge Regression Pratik Rathore Zachary Frangella Madeleine Udell Michał Dereziński Madeleine Udell 42 1 0 14 Jul 2024
Towards Exact Computation of Inductive Bias Akhilan Boopathy William Yue Jaedong Hwang Abhiram Iyer Ila Fiete 42 0 0 22 Jun 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
Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes J. Lin Shreyas Padhy Bruno Mlodozeniec José Miguel Hernández-Lobato 29 1 0 28 May 2024
A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Lifting Masaki Adachi Satoshi Hayakawa Martin Jørgensen Saad Hamid Harald Oberhauser Michael A. Osborne GP 32 3 0 18 Apr 2024
Recommendations for Baselines and Benchmarking Approximate Gaussian Processes Sebastian W. Ober A. Artemev Marcel Wagenlander Rudolfs Grobins Mark van der Wilk GP 18 1 0 15 Feb 2024
Active Few-Shot Fine-Tuning Jonas Hübotter Bhavya Sukhija Lenart Treven Yarden As Andreas Krause 45 1 0 13 Feb 2024
A General Framework for User-Guided Bayesian Optimization Carl Hvarfner Frank Hutter Luigi Nardi GP 47 12 0 24 Nov 2023
Early-Exit Neural Networks with Nested Prediction Sets Metod Jazbec Patrick Forré Stephan Mandt Dan Zhang Eric T. Nalisnick UQCV 37 1 0 10 Nov 2023
Stochastic Gradient Descent for Gaussian Processes Done Right J. Lin Shreyas Padhy Javier Antorán Austin Tripp Alexander Terenin Csaba Szepesvári José Miguel Hernández-Lobato David Janz 16 8 0 31 Oct 2023
Large-Scale Gaussian Processes via Alternating Projection Kaiwen Wu Jonathan Wenger Haydn Thomas Jones Geoff Pleiss Jacob R. Gardner 40 7 0 26 Oct 2023
Trigonometric Quadrature Fourier Features for Scalable Gaussian Process Regression Kevin Li Max Balakirsky Simon Mak 22 2 0 23 Oct 2023
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes M. Noack Hengrui Luo M. Risser GP 32 11 0 18 Sep 2023
Online Laplace Model Selection Revisited J. Lin Javier Antorán José Miguel Hernández-Lobato BDL 32 3 0 12 Jul 2023
Variational Linearized Laplace Approximation for Bayesian Deep Learning Luis A. Ortega Simón Rodríguez Santana Daniel Hernández-Lobato BDL UQCV 47 4 0 24 Feb 2023
On the Convergence of Stochastic Gradient Descent for Linear Inverse Problems in Banach Spaces Ž. Kereta Bangti Jin 26 6 0 10 Feb 2023