David Ginsbourger

Computational Theory and Mathematics · 2010–2013

A researcher in the Science corpus: 11 papers; trusts 53 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 81 citations into it; #1,949 of 12,182 by trust here, #912 by citations
• Statistics and Probability — 62 citations into it; #4,892 of 12,932 by trust here, #1,706 by citations
• Management Science and Operations Research — 58 citations into it; #2,030 of 11,946 by trust here, #1,155 by citations
• Artificial Intelligence — 41 citations into it; #22,271 of 35,345 by trust here, #17,110 by citations
• Mathematical Optimization — 19 citations into it
• Environmental Engineering — 9 citations into it
• Industrial and Manufacturing Engineering — 9 citations into it
• Statistical and Nonlinear Physics — 7 citations into it

Whom David Ginsbourger trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Donald R. Jones
2. Anthony OʼHagan
3. Deng Huang
4. Michael L. Stein
5. Jay D. Martin
6. Marc C. Kennedy
7. Nikolaus Hansen
8. Matthias Schonlau
9. Jeremy E. Oakley
10. Robert B. Gramacy

Who trusts David Ginsbourger overall

1. Peter I. Frazier
2. Tong Yu
3. Kirthevasan Kandasamy
4. Hong Zhu
5. José Miguel Hernández-Lobato
6. James T. Wilson
7. Benjamin Letham
8. Thomas Desautels
9. Jasper Snoek
10. Hamidreza Hamdi

Papers from David Ginsbourger's perspective

Ordered by how strongly the researchers David Ginsbourger trusts endorse each paper. Same corpus, a different order for every researcher.

1. Efficient Global Optimization of Expensive Black-Box Functions
2. Design and analysis of computer experiments
3. A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code
4. A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise
5. Global versus local search in constrained optimization of computer models
6. Predicting the output from a complex computer code when fast approximations are available
7. A Taxonomy of Global Optimization Methods Based on Response Surfaces
8. Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models
9. Exploration of Metamodeling Sampling Criteria for Constrained Global Optimization
10. SDO : A Statistical Method for Global Optimization

Their papers

• Additive Kernels for Gaussian Process Modeling (2011) — 28 citations
• A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations (2013) — 59 citations
• Kriging is well-suited to parallelize optimization (2010) — 474 citations
• ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis (2011) — 90 citations
• A benchmark of kriging-based infill criteria for noisy optimization (2013) — 403 citations
• Quantile-Based Optimization of Noisy Computer Experiments With Tunable Precision (2013) — 216 citations
• DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization (2012) — 664 citations
• Sequential design of computer experiments for the estimation of a probability of failure (2010) — 386 citations
• Adaptive Designs of Experiments for Accurate Approximation of a Target Region (2010) — 330 citations
• Corrected Kriging Update Formulae for Batch-Sequential Data Assimilation (2012) — 55 citations
• Dealing with asynchronicity in parallel Gaussian Process based global optimization (2010) — 64 citations