Robert B. Gramacy

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Mickael BinoisInria Sophia Antipolis - MéditerranéeVerified email at inria.fr
Nicholas W WatkinsVisiting Professor, LSEVerified email at lse.ac.uk
Christian L. E. FranzkeCenter for Climate Physics, Institute for Basic Science (IBS), Pusan National UniversityVerified email at pusan.ac.kr
Stefan M. WildBerkeley Lab, Applied Mathematics & Computational Research DivisionVerified email at lbl.gov
Furong SunAmazonVerified email at amazon.com
Sébastien Le DigabelGERAD and Polytechnique MontréalVerified email at polymtl.ca
Mike LudkovskiProfessor, UC Santa BarbaraVerified email at pstat.ucsb.edu
Leah R. JohnsonVirginia TechVerified email at vt.edu
Nick PolsonProfessor of Econometrics and Statistics, University of ChicagoVerified email at chicagobooth.edu
Jiangeng HuangAbbVieVerified email at abbvie.com
Ben HaalandUniversity of UtahVerified email at hsc.utah.edu
Finn LindgrenUniversity of EdinburghVerified email at ed.ac.uk
Reinhard FurrerProfessor of Applied Statistics, University of ZurichVerified email at math.uzh.ch
Matthias KatzfussProfessor of Statistics, University of Wisconsin–MadisonVerified email at wisc.edu
Matthew HeatonAssociate Professor, Department of Statistics, Brigham Young UniversityVerified email at stat.byu.edu
Abhirup DattaAssociate Professor, Biostatistics, Bloomberg School of Public Health, Johns Hopkins UniversityVerified email at jhu.edu
Andrew FinleyDepartment of Forestry, Michigan State UniversityVerified email at msu.edu
Andrew Zammit MangionUniversity of WollongongVerified email at uow.edu.au
Douglas NychkaProfessor, Applied Mathematics and Statistics, Colorado School of MinesVerified email at mines.edu
Dorit HammerlingAssociate Professor of StatisticsVerified email at mines.edu

Robert B. Gramacy

Virginia Tech

Verified email at vt.edu - Homepage

statistics machine learning optimization uncertainty quantification


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Bayesian treed Gaussian process models with an application to computer modeling RB Gramacy, HKH Lee Journal of the American Statistical Association 103 (483), 1119-1130, 2008	962	2008
Surrogates: Gaussian process modeling, design, and optimization for the applied sciences RB Gramacy Chapman and Hall/CRC, 2020	658	2020
A case study competition among methods for analyzing large spatial data MJ Heaton, A Datta, AO Finley, R Furrer, J Guinness, R Guhaniyogi, ... Journal of Agricultural, Biological and Environmental Statistics 24, 398-425, 2019	487*	2019
Local Gaussian process approximation for large computer experiments RB Gramacy, DW Apley Journal of Computational and Graphical Statistics 24 (2), 561-578, 2015	448	2015
Cases for the nugget in modeling computer experiments RB Gramacy, HKH Lee Statistics and Computing 22, 713-722, 2012	346	2012
Adaptive design and analysis of supercomputer experiments RB Gramacy, HKH Lee Technometrics 51 (2), 130-145, 2009	300	2009
tgp: an R package for Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian process models RB Gramacy Journal of Statistical Software 19, 1-46, 2007	266	2007
Categorical inputs, sensitivity analysis, optimization and importance tempering with tgp version 2, an R package for treed Gaussian process models RB Gramacy, M Taddy Journal of Statistical Software 33, 1-48, 2010	263*	2010
Importance tempering R Gramacy, R Samworth, R King Statistics and Computing 20 (1), 1-7, 2010	253	2010
Practical heteroscedastic Gaussian process modeling for large simulation experiments M Binois, RB Gramacy, M Ludkovski Journal of Computational and Graphical Statistics 27 (4), 808-821, 2018	225	2018
An open challenge to advance probabilistic forecasting for dengue epidemics MA Johansson, KM Apfeldorf, S Dobson, J Devita, AL Buczak, B Baugher, ... Proceedings of the National Academy of Sciences 116 (48), 24268-24274, 2019	215	2019
Optimization under unknown constraints RB Gramacy, HKH Lee Bayesian Statistics 9 9, 229, 2011	199*	2011
A brief history of long memory: Hurst, Mandelbrot and the road to ARFIMA, 1951–1980 T Graves, R Gramacy, N Watkins, C Franzke Entropy 19 (9), 437, 2017	183	2017
laGP: large-scale spatial modeling via local approximate Gaussian processes in R RB Gramacy Journal of Statistical Software 72, 1-46, 2016	181	2016
Modeling an augmented Lagrangian for blackbox constrained optimization RB Gramacy, GA Gray, S Le Digabel, HKH Lee, P Ranjan, G Wells, ... Technometrics 58 (1), 1-11, 2016	171	2016
Dynamic trees for learning and design MA Taddy, RB Gramacy, NG Polson Journal of the American Statistical Association 106 (493), 109-123, 2011	155	2011
Particle learning of Gaussian process models for sequential design and optimization RB Gramacy, NG Polson Journal of Computational and Graphical Statistics 20 (1), 102-118, 2011	148	2011
ACME: Adaptive Caching Using Multiple Experts I Ari, A Amer, RB Gramacy, EL Miller, SA Brandt, DDE Long WDAS, 143-158, 2002	148	2002
Replication or exploration? Sequential design for stochastic simulation experiments M Binois, J Huang, RB Gramacy, M Ludkovski Technometrics 61 (1), 7-23, 2019	145	2019
Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian V Picheny, RB Gramacy, S Wild, S Le Digabel Advances in neural information processing systems 29, 2016	126	2016

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