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1711.03481
Cited By
Scalable Log Determinants for Gaussian Process Kernel Learning
9 November 2017
Kun Dong
David Eriksson
H. Nickisch
D. Bindel
A. Wilson
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Papers citing
"Scalable Log Determinants for Gaussian Process Kernel Learning"
50 / 58 papers shown
Title
Scalable Gaussian Processes with Latent Kronecker Structure
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BOLT: Block-Orthonormal Lanczos for Trace estimation of matrix functions
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Scalable Computations for Generalized Mixed Effects Models with Crossed Random Effects Using Krylov Subspace Methods
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Fabio Sigrist
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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
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0
21 Apr 2025
Robust Gaussian Processes via Relevance Pursuit
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Elizabeth Santorella
David Eriksson
Ben Letham
Maximilian Balandat
E. Bakshy
GP
69
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08 Jan 2025
Batch Active Learning in Gaussian Process Regression using Derivatives
Hon Sum Alec Yu
Christoph Zimmer
D. Nguyen-Tuong
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67
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03 Aug 2024
Gradients of Functions of Large Matrices
Nicholas Krämer
Pablo Moreno-Muñoz
Hrittik Roy
Søren Hauberg
59
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27 May 2024
Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data
Tim Gyger
Reinhard Furrer
Fabio Sigrist
53
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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
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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
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17 Oct 2022
Joint Entropy Search for Multi-objective Bayesian Optimization
Ben Tu
Axel Gandy
N. Kantas
B. Shafei
93
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06 Oct 2022
Optimal Query Complexities for Dynamic Trace Estimation
David P. Woodruff
Fred Zhang
Qiuyi Zhang
49
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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
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19 Jun 2022
Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation
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Carla P. Gomes
62
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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
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04 Oct 2021
Surveillance Evasion Through Bayesian Reinforcement Learning
Dongping Qi
D. Bindel
A. Vladimirsky
15
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30 Sep 2021
Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition
Simon Bartels
Wouter Boomsma
J. Frellsen
Damien Garreau
71
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22 Jul 2021
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization
Jonathan Wenger
Geoff Pleiss
Philipp Hennig
John P. Cunningham
Jacob R. Gardner
101
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0
01 Jul 2021
Active Learning for Deep Neural Networks on Edge Devices
Yuya Senzaki
Christian Hamelain
70
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0
21 Jun 2021
The Fast Kernel Transform
J. Ryan
Sebastian Ament
Carla P. Gomes
Anil Damle
45
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0
08 Jun 2021
Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression
Christian Fiedler
C. Scherer
Sebastian Trimpe
GP
77
70
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06 May 2021
Bias-Free Scalable Gaussian Processes via Randomized Truncations
Andres Potapczynski
Luhuan Wu
D. Biderman
Geoff Pleiss
John P. Cunningham
75
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12 Feb 2021
Faster Kernel Interpolation for Gaussian Processes
Mohit Yadav
Daniel Sheldon
Cameron Musco
BDL
42
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Sensitivity Prewarping for Local Surrogate Modeling
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M. Binois
R. Gramacy
46
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Gauss-Legendre Features for Gaussian Process Regression
Paz Fink Shustin
H. Avron
GP
57
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04 Jan 2021
Quantum algorithms for spectral sums
Alessandro Luongo
Changpeng Shao
53
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12 Nov 2020
Scalable Bayesian Optimization with Sparse Gaussian Process Models
Ang Yang
50
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26 Oct 2020
Hutch++: Optimal Stochastic Trace Estimation
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Cameron Musco
Christopher Musco
David P. Woodruff
96
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19 Oct 2020
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
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
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05 Jun 2020
Scalable Constrained Bayesian Optimization
David Eriksson
Matthias Poloczek
91
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20 Feb 2020
Randomly Projected Additive Gaussian Processes for Regression
Ian A. Delbridge
D. Bindel
A. Wilson
65
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30 Dec 2019
Scalable Gaussian Process Regression for Kernels with a Non-Stationary Phase
J. Grasshoff
Alexandra Jankowski
P. Rostalski
48
3
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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
8
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
47
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
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Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction
William Herlands
Daniel B. Neill
H. Nickisch
A. Wilson
OOD
62
2
0
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GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Jacob R. Gardner
Geoff Pleiss
D. Bindel
Kilian Q. Weinberger
A. Wilson
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149
1,105
0
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