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2020 – today
- 2024
[j31]Christine M. Anderson-Cook
, Lu Lu, Robert B. Gramacy, L. Allison Jones-Farmer, Douglas C. Montgomery, William H. Woodall:
Publishing an applied statistics paper: Guidance and advice from editors. Qual. Reliab. Eng. Int. 40(4): 1918-1934 (2024)- [i7]Nathan Wycoff, John W. Smith, Annie S. Booth, Robert B. Gramacy:
Voronoi Candidates for Bayesian Optimization. CoRR abs/2402.04922 (2024) - 2023
- [j30]Annie Sauer, Andrew Cooper, Robert B. Gramacy:
Vecchia-Approximated Deep Gaussian Processes for Computer Experiments. J. Comput. Graph. Stat. 32(3): 824-837 (2023) - [j29]Ryan B. Christianson, Ryan M. Pollyea, Robert B. Gramacy:
Traditional kriging versus modern Gaussian processes for large-scale mining data. Stat. Anal. Data Min. 16(5): 488-506 (2023) - [j28]Annie Sauer, Robert B. Gramacy, David Higdon:
Active Learning for Deep Gaussian Process Surrogates. Technometrics 65(1): 4-18 (2023) - [i6]Chiwoo Park, Robert Waelder, Bonggwon Kang, Benji Maruyama, Soondo Hong, Robert B. Gramacy:
Active Learning of Piecewise Gaussian Process Surrogates. CoRR abs/2301.08789 (2023) - [i5]Ryan B. Christianson, Robert B. Gramacy:
Robust expected improvement for Bayesian optimization. CoRR abs/2302.08612 (2023) - 2022
- [j27]D. Austin Cole, Robert B. Gramacy, Mike Ludkovski:
Large-scale local surrogate modeling of stochastic simulation experiments. Comput. Stat. Data Anal. 174: 107537 (2022) - [j26]Nathan Wycoff, Mickaël Binois, Robert B. Gramacy:
Sensitivity Prewarping for Local Surrogate Modeling. Technometrics 64(4): 535-547 (2022) - [j25]Jiangeng Huang, Robert B. Gramacy:
Multi-Output Calibration of a Honeycomb Seal via On-site Surrogates. Technometrics 64(4): 548-563 (2022) - [c9]Robert B. Gramacy, Annie Sauer, Nathan Wycoff:
Triangulation candidates for Bayesian optimization. NeurIPS 2022 - 2021
- [j24]Mickaël Binois, Robert B. Gramacy:
hetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R. J. Stat. Softw. 98(1) (2021) - [j23]D. Austin Cole, Ryan B. Christianson, Robert B. Gramacy:
Locally induced Gaussian processes for large-scale simulation experiments. Stat. Comput. 31(3): 33 (2021) - [j22]Adam M. Edwards, Robert B. Gramacy:
Precision aggregated local models. Stat. Anal. Data Min. 14(6): 676-697 (2021) - [j21]Boya Zhang, D. Austin Cole, Robert B. Gramacy:
Distance-Distributed Design for Gaussian Process Surrogates. Technometrics 63(1): 40-52 (2021) - [i4]Nathan Wycoff, Mickaël Binois, Robert B. Gramacy:
Sensitivity Prewarping for Local Surrogate Modeling. CoRR abs/2101.06296 (2021) - [i3]Robert B. Gramacy, Annie Sauer, Nathan Wycoff:
Triangulation candidates for Bayesian optimization. CoRR abs/2112.07457 (2021)
2010 – 2019
- 2019
- [j20]Furong Sun, Robert B. Gramacy, Benjamin Haaland, Earl Lawrence, Andrew Walker:
Emulating Satellite Drag from Large Simulation Experiments. SIAM/ASA J. Uncertain. Quantification 7(2): 720-759 (2019) - [j19]Furong Sun, Robert B. Gramacy, Benjamin Haaland, Siyuan Lu, Youngdeok Hwang:
Synthesizing simulation and field data of solar irradiance. Stat. Anal. Data Min. 12(4): 311-324 (2019) - [j18]Matthias Chung, Mickaël Binois, Robert B. Gramacy, Johnathan M. Bardsley, David J. Moquin, Amanda P. Smith, Amber M. Smith:
Parameter and Uncertainty Estimation for Dynamical Systems Using Surrogate Stochastic Processes. SIAM J. Sci. Comput. 41(4): A2212-A2238 (2019) - [j17]Mickaël Binois, Jiangeng Huang, Robert B. Gramacy, Mike Ludkovski:
Replication or Exploration? Sequential Design for Stochastic Simulation Experiments. Technometrics 61(1): 7-23 (2019) - 2017
- [j16]Timothy Graves, Robert B. Gramacy, Nicholas W. Watkins, Christian Franzke:
A Brief History of Long Memory: Hurst, Mandelbrot and the Road to ARFIMA, 1951-1980. Entropy 19(9): 437 (2017) - 2016
- [j15]Robert B. Gramacy, Genetha A. Gray, Sébastien Le Digabel, Herbert K. H. Lee, Pritam Ranjan, Garth N. Wells, Stefan M. Wild:
Modeling an Augmented Lagrangian for Blackbox Constrained Optimization. Technometrics 58(1): 1-11 (2016) - [j14]Robert B. Gramacy, Genetha A. Gray, Sébastien Le Digabel, Herbert K. H. Lee, Pritam Ranjan, Garth N. Wells, Stefan M. Wild:
Rejoinder. Technometrics 58(1): 26-29 (2016) - [j13]Robert B. Gramacy, Benjamin Haaland:
Speeding Up Neighborhood Search in Local Gaussian Process Prediction. Technometrics 58(3): 294-303 (2016) - [c8]Victor Picheny, Robert B. Gramacy, Stefan M. Wild, Sébastien Le Digabel:
Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian. NIPS 2016: 1435-1443 - 2015
- [j12]Robert B. Gramacy, Michael Ludkovski:
Sequential Design for Optimal Stopping Problems. SIAM J. Financial Math. 6(1): 748-775 (2015) - 2014
- [j11]Robert B. Gramacy, Jarad Niemi, Robin Weiss:
Massively Parallel Approximate Gaussian Process Regression. SIAM/ASA J. Uncertain. Quantification 2(1): 564-584 (2014) - [i2]Ricardo Bezerra de Andrade e Silva, Robert B. Gramacy:
Gaussian Process Structural Equation Models with Latent Variables. CoRR abs/1408.2042 (2014) - 2013
- [j10]Christoforos Anagnostopoulos, Robert B. Gramacy:
Information-Theoretic Data Discarding for Dynamic Trees on Data Streams. Entropy 15(12): 5510-5535 (2013) - [j9]Yuao Hu, Robert B. Gramacy, Heng Lian:
Bayesian quantile regression for single-index models. Stat. Comput. 23(4): 437-454 (2013) - [j8]Robert B. Gramacy:
Comment: On Advances in Expected Improvement. Technometrics 55(1): 19-20 (2013) - [j7]Hugh A. Chipman, Edward I. George, Robert B. Gramacy, Robert E. McCulloch:
Bayesian treed response surface models. WIREs Data Mining Knowl. Discov. 3(4): 298-305 (2013) - [c7]Prasanna Balaprakash, Robert B. Gramacy, Stefan M. Wild:
Active-learning-based surrogate models for empirical performance tuning. CLUSTER 2013: 1-8 - [c6]Prasanna Balaprakash, Karl Rupp, Azamat Mametjanov, Robert B. Gramacy, Paul D. Hovland, Stefan M. Wild:
Empirical performance modeling of GPU kernels using active learning. PARCO 2013: 646-655 - [i1]Robert B. Gramacy, Jarad Niemi, Robin Weiss:
Massively parallel approximate Gaussian process regression. CoRR abs/1310.5182 (2013) - 2012
- [j6]Robert B. Gramacy, Herbert K. H. Lee:
Cases for the nugget in modeling computer experiments. Stat. Comput. 22(3): 713-722 (2012) - [j5]Robert B. Gramacy, Heng Lian:
Gaussian Process Single-Index Models as Emulators for Computer Experiments. Technometrics 54(1): 30-41 (2012) - 2011
- [j4]Tamara Broderick, Robert B. Gramacy:
Classification and Categorical Inputs with Treed Gaussian Process Models. J. Classif. 28(2): 244-270 (2011) - 2010
- [j3]Robert B. Gramacy, Richard Samworth, Ruth King:
Importance tempering. Stat. Comput. 20(1): 1-7 (2010) - [c5]Ricardo Bezerra de Andrade e Silva, Robert B. Gramacy:
Gaussian Process Structural Equation Models with Latent Variables. UAI 2010: 537-545
2000 – 2009
- 2009
- [j2]Robert B. Gramacy, Herbert K. H. Lee:
Adaptive Design and Analysis of Supercomputer Experiments. Technometrics 51(2): 130-145 (2009) - [c4]Ricardo Bezerra de Andrade e Silva, Robert B. Gramacy:
MCMC Methods for Bayesian Mixtures of Copulas. AISTATS 2009: 512-519 - 2008
- [j1]Robert B. Gramacy, Herbert K. H. Lee:
Gaussian processes and limiting linear models. Comput. Stat. Data Anal. 53(1): 123-136 (2008) - 2004
- [c3]Robert B. Gramacy, Herbert K. H. Lee, William G. Macready:
Parameter space exploration with Gaussian process trees. ICML 2004 - 2002
- [c2]Robert B. Gramacy, Manfred K. Warmuth, Scott A. Brandt, Ismail Ari:
Adaptive Caching by Refetching. NIPS 2002: 1465-1472 - [c1]Ismail Ari, Ahmed Amer, Robert B. Gramacy, Ethan L. Miller, Scott A. Brandt, Darrell D. E. Long:
ACME: Adaptive Caching Using Multiple Experts. WDAS 2002: 143-158
Coauthor Index
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last updated on 2024-10-07 22:20 CEST by the dblp team
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