Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)

3 March 2015

Papers citing "Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)"

26 / 76 papers shown

Title
UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data Chacha Chen Junjie Liang Fenglong Ma Lucas Glass Jimeng Sun Cao Xiao 19 26 0 22 Oct 2020
KrigHedge: Gaussian Process Surrogates for Delta Hedging M. Ludkovski Yuri F. Saporito 43 5 0 16 Oct 2020
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization Geoff Pleiss M. Jankowiak David Eriksson Anil Damle Jacob R. Gardner 19 43 0 19 Jun 2020
Kernel methods through the roof: handling billions of points efficiently Giacomo Meanti Luigi Carratino Lorenzo Rosasco Alessandro Rudi 22 112 0 18 Jun 2020
The Statistical Cost of Robust Kernel Hyperparameter Tuning R. A. Meyer Christopher Musco 16 2 0 14 Jun 2020
Deep Gaussian Markov Random Fields Per Sidén Fredrik Lindsten BDL 28 22 0 18 Feb 2020
Randomly Projected Additive Gaussian Processes for Regression Ian A. Delbridge D. Bindel A. Wilson 24 27 0 30 Dec 2019
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization Maximilian Balandat Brian Karrer Daniel R. Jiang Sam Daulton Benjamin Letham A. Wilson E. Bakshy 32 93 0 14 Oct 2019
Adaptive Deep Kernel Learning Prudencio Tossou Basile Dura François Laviolette M. Marchand Alexandre Lacoste 27 29 0 28 May 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 8 225 0 19 Mar 2019
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data Yinhao Zhu N. Zabaras P. Koutsourelakis P. Perdikaris PINN AI4CE 46 854 0 18 Jan 2019
Neural Non-Stationary Spectral Kernel Sami Remes Markus Heinonen Samuel Kaski BDL 16 9 0 27 Nov 2018
Scaling Gaussian Process Regression with Derivatives David Eriksson Kun Dong E. Lee D. Bindel A. Wilson GP 14 75 0 29 Oct 2018
Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion Steven Atkinson N. Zabaras 14 36 0 11 Jul 2018
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees Jonathan H. Huggins Trevor Campbell Mikolaj Kasprzak Tamara Broderick 35 15 0 26 Jun 2018
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression Haitao Liu Jianfei Cai Yi Wang Yew-Soon Ong 23 83 0 03 Jun 2018
Constant-Time Predictive Distributions for Gaussian Processes Geoff Pleiss Jacob R. Gardner Kilian Q. Weinberger A. Wilson 25 94 0 16 Mar 2018
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition Pavel Izmailov Alexander Novikov D. Kropotov 20 61 0 19 Oct 2017
Gaussian process regression for forecasting battery state of health R. Richardson Michael A. Osborne David A. Howey 9 519 0 16 Mar 2017
Learning Scalable Deep Kernels with Recurrent Structure Maruan Al-Shedivat A. Wilson Yunus Saatchi Zhiting Hu Eric P. Xing BDL 13 104 0 27 Oct 2016
Warm Starting Bayesian Optimization Matthias Poloczek Jialei Wang P. Frazier 15 61 0 11 Aug 2016
Preconditioning Kernel Matrices Kurt Cutajar Michael A. Osborne John P. Cunningham Maurizio Filippone 26 72 0 22 Feb 2016
Deep Kernel Learning A. Wilson Zhiting Hu Ruslan Salakhutdinov Eric P. Xing BDL 47 872 0 06 Nov 2015
Blitzkriging: Kronecker-structured Stochastic Gaussian Processes T. Nickson Tom Gunter C. Lloyd Michael A. Osborne Stephen J. Roberts 11 21 0 27 Oct 2015
A Practical Guide to Randomized Matrix Computations with MATLAB Implementations Shusen Wang 27 37 0 28 May 2015
A Framework for Evaluating Approximation Methods for Gaussian Process Regression Krzysztof Chalupka Christopher K. I. Williams Iain Murray GP 71 169 0 29 May 2012