Stochastic Gradient Descent for Gaussian Processes Done Right

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Stochastic Gradient Descent for Gaussian Processes Done Right

31 October 2023

Javier Antorán

Alexander Terenin

Csaba Szepesvári

José Miguel Hernández-Lobato

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Papers citing "Stochastic Gradient Descent for Gaussian Processes Done Right"

14 / 14 papers shown

Title
Joker: Joint Optimization Framework for Lightweight Kernel Machines Junhong Zhang Zhihui Lai 48 0 0 23 May 2025
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes J. Lin Shreyas Padhy Bruno Mlodozeniec Javier Antorán José Miguel Hernández-Lobato 69 3 0 28 May 2024
Tanimoto Random Features for Scalable Molecular Machine Learning Austin Tripp S. Bacallado Sukriti Singh José Miguel Hernández-Lobato 48 8 0 26 Jun 2023
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent J. Lin Javier Antorán Shreyas Padhy David Janz José Miguel Hernández-Lobato Alexander Terenin 77 25 0 20 Jun 2023
Sampling-based inference for large linear models, with application to linearised Laplace Javier Antorán Shreyas Padhy Riccardo Barbano Eric T. Nalisnick David Janz José Miguel Hernández-Lobato BDL 40 17 0 10 Oct 2022
DOCKSTRING: easy molecular docking yields better benchmarks for ligand design Miguel García-Ortegón G. Simm Austin Tripp José Miguel Hernández-Lobato A. Bender S. Bacallado 86 82 0 29 Oct 2021
Efficiently Sampling Functions from Gaussian Process Posteriors James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 56 165 0 21 Feb 2020
Exact Gaussian Processes on a Million Data Points Ke Alexander Wang Geoff Pleiss Jacob R. Gardner Stephen Tyree Kilian Q. Weinberger A. Wilson GP 55 230 0 19 Mar 2019
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space José Miguel Hernández-Lobato James Requeima Edward O. Pyzer-Knapp Alán Aspuru-Guzik 76 183 0 06 Jun 2017
FALKON: An Optimal Large Scale Kernel Method Alessandro Rudi Luigi Carratino Lorenzo Rosasco 78 196 0 31 May 2017
Neural Message Passing for Quantum Chemistry Justin Gilmer S. Schoenholz Patrick F. Riley Oriol Vinyals George E. Dahl 593 7,455 0 04 Apr 2017
Gaussian Processes for Big Data J. Hensman Nicolò Fusi Neil D. Lawrence GP 107 1,232 0 26 Sep 2013
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization Shai Shalev-Shwartz Tong Zhang 184 1,033 0 10 Sep 2012
Greedy Block Coordinate Descent for Large Scale Gaussian Process Regression Liefeng Bo C. Sminchisescu 55 25 0 13 Jun 2012