ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1711.03481
  4. Cited By
Scalable Log Determinants for Gaussian Process Kernel Learning

Scalable Log Determinants for Gaussian Process Kernel Learning

9 November 2017
Kun Dong
David Eriksson
H. Nickisch
D. Bindel
A. Wilson
ArXiv (abs)PDFHTML

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

50 / 58 papers shown
Title
Scalable Gaussian Processes with Latent Kronecker Structure
Scalable Gaussian Processes with Latent Kronecker Structure
Jihao Andreas Lin
Sebastian Ament
Maximilian Balandat
David Eriksson
José Miguel Hernández-Lobato
E. Bakshy
7
0
0
07 Jun 2025
BOLT: Block-Orthonormal Lanczos for Trace estimation of matrix functions
BOLT: Block-Orthonormal Lanczos for Trace estimation of matrix functions
Kingsley Yeon
Promit Ghosal
Mihai Anitescu
238
0
0
18 May 2025
Scalable Computations for Generalized Mixed Effects Models with Crossed Random Effects Using Krylov Subspace Methods
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
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
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
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
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
Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data
Tim Gyger
Reinhard Furrer
Fabio Sigrist
53
2
0
23 May 2024
Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian
  Process Models
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
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
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
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
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
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
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
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
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
Implicit Riemannian Concave Potential Maps
Danilo Jimenez Rezende
S. Racanière
AI4CE
93
7
0
04 Oct 2021
Surveillance Evasion Through Bayesian Reinforcement Learning
Surveillance Evasion Through Bayesian Reinforcement Learning
Dongping Qi
D. Bindel
A. Vladimirsky
15
0
0
30 Sep 2021
Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition
Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition
Simon Bartels
Wouter Boomsma
J. Frellsen
Damien Garreau
71
4
0
22 Jul 2021
Preconditioning for Scalable Gaussian Process Hyperparameter
  Optimization
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization
Jonathan Wenger
Geoff Pleiss
Philipp Hennig
John P. Cunningham
Jacob R. Gardner
101
24
0
01 Jul 2021
Active Learning for Deep Neural Networks on Edge Devices
Active Learning for Deep Neural Networks on Edge Devices
Yuya Senzaki
Christian Hamelain
70
7
0
21 Jun 2021
The Fast Kernel Transform
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
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
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
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
Sensitivity Prewarping for Local Surrogate Modeling
Nathan Wycoff
M. Binois
R. Gramacy
46
10
0
15 Jan 2021
Gauss-Legendre Features for Gaussian Process Regression
Gauss-Legendre Features for Gaussian Process Regression
Paz Fink Shustin
H. Avron
GP
57
11
0
04 Jan 2021
Quantum algorithms for spectral sums
Quantum algorithms for spectral sums
Alessandro Luongo
Changpeng Shao
53
5
0
12 Nov 2020
Scalable Bayesian Optimization with Sparse Gaussian Process Models
Scalable Bayesian Optimization with Sparse Gaussian Process Models
Ang Yang
50
0
0
26 Oct 2020
Hutch++: Optimal Stochastic Trace Estimation
Hutch++: Optimal Stochastic Trace Estimation
R. A. Meyer
Cameron Musco
Christopher Musco
David P. Woodruff
96
106
0
19 Oct 2020
CorrAttack: Black-box Adversarial Attack with Structured Search
CorrAttack: Black-box Adversarial Attack with Structured Search
Zhichao Huang
Yaowei Huang
Tong Zhang
AAML
53
8
0
03 Oct 2020
Non-Stationary Multi-layered Gaussian Priors for Bayesian Inversion
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
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
Sparse Gaussian Processes via Parametric Families of Compactly-supported Kernels
Jarred Barber
GP
31
2
0
05 Jun 2020
Scalable Constrained Bayesian Optimization
Scalable Constrained Bayesian Optimization
David Eriksson
Matthias Poloczek
91
102
0
20 Feb 2020
Randomly Projected Additive Gaussian Processes for Regression
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
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
A literature survey of matrix methods for data science
Martin Stoll
58
20
0
17 Dec 2019
Conjugate Gradients for Kernel Machines
Conjugate Gradients for Kernel Machines
Simon Bartels
Philipp Hennig
66
4
0
14 Nov 2019
Sparse inversion for derivative of log determinant
Sparse inversion for derivative of log determinant
Shengxin Zhu
A. Wathen
8
5
0
02 Nov 2019
Function-Space Distributions over Kernels
Function-Space Distributions over Kernels
Gregory W. Benton
Wesley J. Maddox
Jayson Salkey
J. Albinati
A. Wilson
BDLGP
53
26
0
29 Oct 2019
Scalable Global Optimization via Local Bayesian Optimization
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
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
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
47
20
0
03 Jun 2019
Exact Gaussian Processes on a Million Data Points
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
Invertible Residual Networks
Jens Behrmann
Will Grathwohl
Ricky T. Q. Chen
David Duvenaud
J. Jacobsen
UQCVTPM
161
624
0
02 Nov 2018
Scaling Gaussian Process Regression with Derivatives
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
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
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Jacob R. Gardner
Geoff Pleiss
D. Bindel
Kilian Q. Weinberger
A. Wilson
GP
149
1,105
0
28 Sep 2018
12
Next