Scalable Log Determinants for Gaussian Process Kernel Learning

9 November 2017

Papers citing "Scalable Log Determinants for Gaussian Process Kernel Learning"

50 / 58 papers shown

Title
Scalable Gaussian Processes with Latent Kronecker Structure Jihao Andreas Lin Sebastian Ament Maximilian Balandat David Eriksson José Miguel Hernández-Lobato E. Bakshy 12 0 0 07 Jun 2025
BOLT: Block-Orthonormal Lanczos for Trace estimation of matrix functions Kingsley Yeon Promit Ghosal Mihai Anitescu 240 0 0 18 May 2025
Scalable Computations for Generalized Mixed Effects Models with Crossed Random Effects Using Krylov Subspace Methods Pascal Kündig Fabio Sigrist 86 0 0 14 May 2025
Compute-Optimal LLMs Provably Generalize Better With Scale Marc Finzi Sanyam Kapoor Diego Granziol Anming Gu Christopher De Sa J. Zico Kolter Andrew Gordon Wilson 129 0 0 21 Apr 2025
Robust Gaussian Processes via Relevance Pursuit Sebastian Ament Elizabeth Santorella David Eriksson Ben Letham Maximilian Balandat E. Bakshy GP 69 1 0 08 Jan 2025
Batch Active Learning in Gaussian Process Regression using Derivatives Hon Sum Alec Yu Christoph Zimmer D. Nguyen-Tuong GP 67 1 0 03 Aug 2024
Gradients of Functions of Large Matrices Nicholas Krämer Pablo Moreno-Muñoz Hrittik Roy Søren Hauberg 59 0 0 27 May 2024
Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data Tim Gyger Reinhard Furrer Fabio Sigrist 57 2 0 23 May 2024
Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models Pascal Kündig Fabio Sigrist 34 3 0 18 Oct 2023
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models Alexander Lin Bahareh Tolooshams Yves Atchadé Demba E. Ba 70 1 0 05 Jun 2023
Spatially scalable recursive estimation of Gaussian process terrain maps using local basis functions Frida Marie Viset Rudy Helmons Manon Kok 96 1 0 17 Oct 2022
Joint Entropy Search for Multi-objective Bayesian Optimization Ben Tu Axel Gandy N. Kantas B. Shafei 93 39 0 06 Oct 2022
Optimal Query Complexities for Dynamic Trace Estimation David P. Woodruff Fred Zhang Qiuyi Zhang 49 4 0 30 Sep 2022
Log-GPIS-MOP: A Unified Representation for Mapping, Odometry and Planning Lan Wu Ki Myung Brian Lee Cedric Le Gentil Teresa Vidal-Calleja 73 22 0 19 Jun 2022
Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation Sebastian Ament Carla P. Gomes 62 9 0 16 Jun 2022
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior Javier Antorán Riccardo Barbano Johannes Leuschner José Miguel Hernández-Lobato Bangti Jin UQCV 112 10 0 28 Feb 2022
Adaptive Cholesky Gaussian Processes Simon Bartels Kristoffer Stensbo-Smidt Pablo Moreno-Muñoz Wouter Boomsma J. Frellsen Søren Hauberg 78 3 0 22 Feb 2022
Implicit Riemannian Concave Potential Maps Danilo Jimenez Rezende S. Racanière AI4CE 93 7 0 04 Oct 2021
Surveillance Evasion Through Bayesian Reinforcement Learning Dongping Qi D. Bindel A. Vladimirsky 17 0 0 30 Sep 2021
Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition Simon Bartels Wouter Boomsma J. Frellsen Damien Garreau 73 4 0 22 Jul 2021
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization Jonathan Wenger Geoff Pleiss Philipp Hennig John P. Cunningham Jacob R. Gardner 104 24 0 01 Jul 2021
Active Learning for Deep Neural Networks on Edge Devices Yuya Senzaki Christian Hamelain 70 7 0 21 Jun 2021
The Fast Kernel Transform J. Ryan Sebastian Ament Carla P. Gomes Anil Damle 45 9 0 08 Jun 2021
Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression Christian Fiedler C. Scherer Sebastian Trimpe GP 77 70 0 06 May 2021
Bias-Free Scalable Gaussian Processes via Randomized Truncations Andres Potapczynski Luhuan Wu D. Biderman Geoff Pleiss John P. Cunningham 75 20 0 12 Feb 2021
Faster Kernel Interpolation for Gaussian Processes Mohit Yadav Daniel Sheldon Cameron Musco BDL 42 10 0 28 Jan 2021
Sensitivity Prewarping for Local Surrogate Modeling Nathan Wycoff M. Binois R. Gramacy 48 10 0 15 Jan 2021
Gauss-Legendre Features for Gaussian Process Regression Paz Fink Shustin H. Avron GP 59 11 0 04 Jan 2021
Quantum algorithms for spectral sums Alessandro Luongo Changpeng Shao 53 5 0 12 Nov 2020
Scalable Bayesian Optimization with Sparse Gaussian Process Models Ang Yang 50 0 0 26 Oct 2020
Hutch++: Optimal Stochastic Trace Estimation R. A. Meyer Cameron Musco Christopher Musco David P. Woodruff 98 106 0 19 Oct 2020
CorrAttack: Black-box Adversarial Attack with Structured Search Zhichao Huang Yaowei Huang Tong Zhang AAML 55 8 0 03 Oct 2020
Non-Stationary Multi-layered Gaussian Priors for Bayesian Inversion M. Emzir Sari Lasanen Z. Purisha L. Roininen Simo Särkkä 78 9 0 28 Jun 2020
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization Geoff Pleiss M. Jankowiak David Eriksson Anil Damle Jacob R. Gardner 80 43 0 19 Jun 2020
Sparse Gaussian Processes via Parametric Families of Compactly-supported Kernels Jarred Barber GP 31 2 0 05 Jun 2020
Scalable Constrained Bayesian Optimization David Eriksson Matthias Poloczek 91 102 0 20 Feb 2020
Randomly Projected Additive Gaussian Processes for Regression Ian A. Delbridge D. Bindel A. Wilson 65 27 0 30 Dec 2019
Scalable Gaussian Process Regression for Kernels with a Non-Stationary Phase J. Grasshoff Alexandra Jankowski P. Rostalski 48 3 0 25 Dec 2019
A literature survey of matrix methods for data science Martin Stoll 58 20 0 17 Dec 2019
Conjugate Gradients for Kernel Machines Simon Bartels Philipp Hennig 66 4 0 14 Nov 2019
Sparse inversion for derivative of log determinant Shengxin Zhu A. Wathen 10 5 0 02 Nov 2019
Function-Space Distributions over Kernels Gregory W. Benton Wesley J. Maddox Jayson Salkey J. Albinati A. Wilson BDL GP 53 26 0 29 Oct 2019
Scalable Global Optimization via Local Bayesian Optimization Samyam Rajbhandari Michael Pearce Jacob R. Gardner Ryan D. Turner Matthias Poloczek 100 474 0 03 Oct 2019
pySOT and POAP: An event-driven asynchronous framework for surrogate optimization David Eriksson D. Bindel C. Shoemaker 75 56 0 30 Jul 2019
MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning Diego Granziol Binxin Ru S. Zohren Xiaowen Dong Michael A. Osborne Stephen J. Roberts 49 20 0 03 Jun 2019
Exact Gaussian Processes on a Million Data Points Ke Alexander Wang Geoff Pleiss Jacob R. Gardner Stephen Tyree Kilian Q. Weinberger A. Wilson GP 62 230 0 19 Mar 2019
Invertible Residual Networks Jens Behrmann Will Grathwohl Ricky T. Q. Chen David Duvenaud J. Jacobsen UQCV TPM 161 624 0 02 Nov 2018
Scaling Gaussian Process Regression with Derivatives David Eriksson Kun Dong E. Lee D. Bindel A. Wilson GP 60 76 0 29 Oct 2018
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction William Herlands Daniel B. Neill H. Nickisch A. Wilson OOD 62 2 0 28 Oct 2018
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration Jacob R. Gardner Geoff Pleiss D. Bindel Kilian Q. Weinberger A. Wilson GP 149 1,106 0 28 Sep 2018