Robert B. Gramacy

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Nicholas W WatkinsVisiting Professor, Centre for the Analysis of Time Series, LSEVerified email at lse.ac.uk
Christian L. E. FranzkeCenter for Climate Physics, Institute for Basic Science (IBS)Verified email at pusan.ac.kr
Stefan M. WildArgonne National Laboratory, Mathematics & Computer Science DivisionVerified email at mcs.anl.gov
Mickael BinoisInria Sophia Antipolis - MéditerranéeVerified email at inria.fr
Furong SunPfizer Inc.Verified email at pfizer.com
Nick PolsonProfessor of Econometrics and Statistics, University of ChicagoVerified email at chicagobooth.edu
Sébastien Le DigabelGERAD and Polytechnique MontréalVerified email at polymtl.ca
Leah R. JohnsonVirginia TechVerified email at vt.edu
Ben HaalandUniversity of UtahVerified email at hsc.utah.edu
Mike LudkovskiProfessor, UC Santa BarbaraVerified email at pstat.ucsb.edu
Jiangeng HuangGenentech-RocheVerified email at gene.com
Scott A. BrandtVice Chancellor for Research, University of California, Santa CruzVerified email at ucsc.edu
Ismail AriAssistant Professor of Computer Science, Ozyegin UniversityVerified email at ozyegin.edu.tr
Finn LindgrenUniversity of EdinburghVerified email at ed.ac.uk
Florian GerberDepartment of Applied Mathematics and Statistics, Colorado School of MinesVerified email at mines.edu
Reinhard FurrerProfessor of Applied Statistics, University of ZurichVerified email at math.uzh.ch
Matthias KatzfussAssociate Professor of Statistics, Texas A&M UniversityVerified email at stat.tamu.edu
Matthew HeatonAssociate Professor, Department of Statistics, Brigham Young UniversityVerified email at stat.byu.edu
Abhirup DattaAssistant 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

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	624	2008
Local Gaussian process approximation for large computer experiments RB Gramacy, DW Apley Journal of Computational and Graphical Statistics 24 (2), 561-578, 2015	234	2015
Cases for the nugget in modeling computer experiments RB Gramacy, HKH Lee Statistics and Computing 22 (3), 713-722, 2012	216	2012
tgp: an R package for Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian process models RB Gramacy Journal of Statistical Software 19 (9), 6, 2007	209	2007
Adaptive design and analysis of supercomputer experiments RB Gramacy, HKH Lee Technometrics 51 (2), 130-145, 2009	198	2009
Importance tempering R Gramacy, R Samworth, R King Statistics and Computing 20 (1), 1-7, 2010	191	2010
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	191*	2010
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 (3), 398-425, 2019	184*	2019
Optimization under unknown constraints RB Gramacy, HKH Lee Bayesian Statistics 9 9, 229, 2011	127*	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	123	2011
ACME: Adaptive Caching Using Multiple Experts I Ari, A Amer, RB Gramacy, EL Miller, SA Brandt, DDE Long WDAS, 143-158, 2002	122	2002
Dynamic trees for learning and design MA Taddy, RB Gramacy, NG Polson Journal of the American Statistical Association 106 (493), 109-123, 2011	118	2011
Bayesian treed Gaussian process models RB Gramacy University of California, Santa Cruz, 2005	112	2005
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	97*	2017
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	95	2018
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	93	2016
Adaptive caching by refetching RB Gramacy, MK Warmuth, SA Brandt, İ Arı Advances in Neural Information Processing Systems 15, 1465–1472, 2003	85	2003
Parameter space exploration with Gaussian process trees RB Gramacy, HKH Lee, WG Macready Proceedings of the twenty-first international conference on Machine learning, 45, 2004	82	2004
laGP: Large-scale spatial modeling via local approximate Gaussian processes in R RB Gramacy Journal of Statistical Software 72 (1), 1-46, 2016	81	2016
Geometry: mesh generation and surface tesselation R Grasman, RB Gramacy R package version 0.1-4. URL http://cran. r-project. org/web/packages/geometry, 2008	67*	2008

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