High-Dimensional Gaussian Process Inference with Derivatives

15 February 2021

Papers citing "High-Dimensional Gaussian Process Inference with Derivatives"

21 / 21 papers shown

Title
Probabilistic Linear Solvers for Machine Learning Jonathan Wenger Philipp Hennig 80 17 0 19 Oct 2020
Deep Bayesian Quadrature Policy Optimization Akella Ravi Tej Kamyar Azizzadenesheli Mohammad Ghavamzadeh Anima Anandkumar Yisong Yue 36 5 0 28 Jun 2020
SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives Emmanouil Angelis Philippe Wenk Bernhard Schölkopf Stefan Bauer Andreas Krause BDL 48 3 0 05 Mar 2020
Stochastic quasi-Newton with line-search regularization A. Wills Thomas B. Schon ODL 58 21 0 03 Sep 2019
Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization Filip de Roos Philipp Hennig AI4CE ODL 61 3 0 20 Feb 2019
Scaling Gaussian Process Regression with Derivatives David Eriksson Kun Dong E. Lee D. Bindel A. Wilson GP 56 76 0 29 Oct 2018
Probabilistic Linear Solvers: A Unifying View Simon Bartels Jon Cockayne Ilse C. F. Ipsen Philipp Hennig 75 24 0 08 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 136 1,104 0 28 Sep 2018
Product Kernel Interpolation for Scalable Gaussian Processes Jacob R. Gardner Geoff Pleiss Ruihan Wu Kilian Q. Weinberger A. Wilson 69 74 0 24 Feb 2018
A Bayesian Conjugate Gradient Method Jon Cockayne Chris J. Oates Ilse C. F. Ipsen Mark Girolami 46 27 0 16 Jan 2018
Neural Network Gradient Hamiltonian Monte Carlo Lingge Li Andrew J Holbrook Babak Shahbaba Pierre Baldi BDL 47 23 0 14 Nov 2017
On the construction of probabilistic Newton-type algorithms A. Wills Thomas B. Schon 51 13 0 05 Apr 2017
Bayesian Optimization with Gradients Jian Wu Matthias Poloczek A. Wilson P. Frazier 65 210 0 13 Mar 2017
Linearly constrained Gaussian processes Carl Jidling Niklas Wahlström A. Wills Thomas B. Schon 56 98 0 02 Mar 2017
Modeling and interpolation of the ambient magnetic field by Gaussian processes Arno Solin Manon Kok Niklas Wahlström Thomas B. Schon Simo Särkkä 31 119 0 15 Sep 2015
Probabilistic Numerics and Uncertainty in Computations Philipp Hennig Michael A. Osborne Mark Girolami 76 307 0 03 Jun 2015
Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP) A. Wilson H. Nickisch GP 86 515 0 03 Mar 2015
Stochastic Gradient Hamiltonian Monte Carlo Tianqi Chen E. Fox Carlos Guestrin BDL 128 913 0 17 Feb 2014
Probabilistic Interpretation of Linear Solvers Philipp Hennig 81 104 0 10 Feb 2014
Hilbert Space Methods for Reduced-Rank Gaussian Process Regression Arno Solin Simo Särkkä 213 218 0 21 Jan 2014
Gaussian Process Kernels for Pattern Discovery and Extrapolation A. Wilson Ryan P. Adams GP 94 611 0 18 Feb 2013