Exact Gaussian Processes on a Million Data Points

19 March 2019

Papers citing "Exact Gaussian Processes on a Million Data Points"

50 / 142 papers shown

Title
Integrative Analysis and Imputation of Multiple Data Streams via Deep Gaussian Processes Ali Akbar Septiandri Deyu Ming F. Alejandro DiazDelaO Takoua Jendoubi Samiran Ray 7 0 0 17 May 2025
Scaling Gaussian Process Regression with Full Derivative Observations Daniel Huang BDL GP 41 0 0 14 May 2025
Adaptive Replication Strategies in Trust-Region-Based Bayesian Optimization of Stochastic Functions Mickael Binois Jeffrey Larson 74 0 0 29 Apr 2025
On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing Samantha Petti Carlos Martí-Gómez Justin B. Kinney Juannan Zhou David M. McCandlish GP 24 0 0 26 Apr 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 237 0 0 26 Jan 2025
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data M. Risser M. Noack Hengrui Luo Ronald Pandolfi GP 38 0 0 07 Nov 2024
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference Jonathan Wenger Kaiwen Wu Philipp Hennig Jacob R. Gardner Geoff Pleiss John P. Cunningham 40 5 0 01 Nov 2024
High-Dimensional Gaussian Process Regression with Soft Kernel Interpolation Chris Camaño Daniel Huang BDL GP 45 1 0 28 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
Embrace rejection: Kernel matrix approximation by accelerated randomly pivoted Cholesky Ethan N. Epperly J. Tropp R. Webber 34 4 0 04 Oct 2024
Gaussian Processes Sampling with Sparse Grids under Additive Schwarz Preconditioner Haoyuan Chen Rui Tuo 40 0 0 01 Aug 2024
MUSE-Net: Missingness-aware mUlti-branching Self-attention Encoder for Irregular Longitudinal Electronic Health Records Zekai Wang Tieming Liu B. Yao 50 0 0 30 Jun 2024
Contraction rates for conjugate gradient and Lanczos approximate posteriors in Gaussian process regression Bernhard Stankewitz Botond Szabo 45 2 0 18 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 46 2 0 28 May 2024
Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes J. Lin Shreyas Padhy Bruno Mlodozeniec José Miguel Hernández-Lobato 37 1 0 28 May 2024
Gradients of Functions of Large Matrices Nicholas Krämer Pablo Moreno-Muñoz Hrittik Roy Søren Hauberg 40 0 0 27 May 2024
Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks Yunzhen Feng Tim G. J. Rudner Nikolaos Tsilivis Julia Kempe AAML BDL 43 1 0 27 Apr 2024
Kermut: Composite kernel regression for protein variant effects Peter Mørch Groth Mads Herbert Kerrn Lars Olsen Jesper Salomon Wouter Boomsma 47 2 0 09 Apr 2024
Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression Lu Zou Liang Ding 39 0 0 20 Mar 2024
Function-space Parameterization of Neural Networks for Sequential Learning Aidan Scannell Riccardo Mereu Paul E. Chang Ella Tamir Joni Pajarinen Arno Solin BDL 34 5 0 16 Mar 2024
Automated Efficient Estimation using Monte Carlo Efficient Influence Functions Raj Agrawal Sam Witty Andy Zane Eli Bingham 40 2 0 29 Feb 2024
Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning Jörn Tebbe Christoph Zimmer A. Steland Markus Lange-Hegermann Fabian Mies GP 32 3 0 28 Feb 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
Variational Elliptical Processes Maria B˙ankestad Jens Sjölund Jalil Taghia Thomas B. Schon 36 2 0 21 Nov 2023
SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes Abdelhak Lemkhenter Manchen Wang L. Zancato Gurumurthy Swaminathan Paolo Favaro Davide Modolo 41 0 0 03 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 18 8 0 31 Oct 2023
Large-Scale Gaussian Processes via Alternating Projection Kaiwen Wu Jonathan Wenger Haydn Thomas Jones Geoff Pleiss Jacob R. Gardner 48 8 0 26 Oct 2023
Deterministic Langevin Unconstrained Optimization with Normalizing Flows James M. Sullivan U. Seljak 29 0 0 01 Oct 2023
Gradient and Uncertainty Enhanced Sequential Sampling for Global Fit Sven Lämmle Can Bogoclu K. Cremanns D. Roos 30 5 0 29 Sep 2023
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra Andres Potapczynski Marc Finzi Geoff Pleiss Andrew Gordon Wilson 20 7 0 06 Sep 2023
FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation S. Bouabid Dino Sejdinovic D. Watson‐Parris 16 5 0 14 Jul 2023
Beyond Intuition, a Framework for Applying GPs to Real-World Data K. Tazi J. Lin Ross Viljoen A. Gardner S. T. John Hong Ge Richard Turner GP 18 3 0 06 Jul 2023
Data-Driven Design for Metamaterials and Multiscale Systems: A Review Doksoo Lee Wei Chen Liwei Wang Yu-Chin Chan Wei Chen AI4CE 35 80 0 01 Jul 2023
Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression Robert Allison Anthony Stephenson F. Samuel Edward O. Pyzer-Knapp UQCV 17 3 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 29 23 0 20 Jun 2023
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models Alexander Lin Bahareh Tolooshams Yves Atchadé Demba E. Ba 36 1 0 05 Jun 2023
Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning Wei Tang Weijia Zhang Min-Ling Zhang 34 9 0 26 May 2023
Privacy-aware Gaussian Process Regression Rui Tuo R. Bhattacharya 14 1 0 25 May 2023
Uniform approximation of common Gaussian process kernels using equispaced Fourier grids A. Barnett P. Greengard M. Rachh 23 7 0 18 May 2023
Robust, randomized preconditioning for kernel ridge regression Mateo Díaz Ethan N. Epperly Zachary Frangella J. Tropp R. Webber 42 12 0 24 Apr 2023
Kernel Regression with Infinite-Width Neural Networks on Millions of Examples Ben Adlam Jaehoon Lee Shreyas Padhy Zachary Nado Jasper Snoek 26 11 0 09 Mar 2023
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations Tim G. J. Rudner Cong Lu Michael A. Osborne Yarin Gal Yee Whye Teh OffRL 38 27 0 28 Dec 2022
Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence Stefan Blücher Klaus-Robert Muller Stefan Chmiela 21 4 0 24 Dec 2022
Multi-Instance Partial-Label Learning: Towards Exploiting Dual Inexact Supervision Wei Tang Weijia Zhang Min-Ling Zhang 19 12 0 18 Dec 2022
Environmental Sensor Placement with Convolutional Gaussian Neural Processes Tom R. Andersson W. Bruinsma Stratis Markou James Requeima Alejandro Coca-Castro ... A. Ellis M. Lazzara Daniel P. Jones Scott Hosking Richard Turner 25 13 0 18 Nov 2022
Equispaced Fourier representations for efficient Gaussian process regression from a billion data points P. Greengard M. Rachh A. Barnett 24 12 0 18 Oct 2022
Computationally-efficient initialisation of GPs: The generalised variogram method Felipe A. Tobar Elsa Cazelles T. Wolff 19 0 0 11 Oct 2022
Log-Linear-Time Gaussian Processes Using Binary Tree Kernels Michael K. Cohen Sam Daulton Michael A. Osborne GP 32 5 0 04 Oct 2022
Bézier Gaussian Processes for Tall and Wide Data Martin Jørgensen Michael A. Osborne GP 21 2 0 01 Sep 2022
Gaussian Process Surrogate Models for Neural Networks Michael Y. Li Erin Grant Thomas Griffiths BDL SyDa 38 7 0 11 Aug 2022