Roustant, Olivier

1. Calculations of Sobol indices for the Gaussian process metamodel
   2008/02/07 by Marrel, Amandine, Iooss, Bertrand, Laurent, Beatrice +1 · 7 citations
   #FOS: Computer and information sciences #FOS: Mathematics #Methodology (stat.ME) #Statistics Theory (math.ST)
2. Group kernels for Gaussian process metamodels with categorical inputs
   2018/02/07 by Roustant, Olivier, Padonou, Esperan, Deville, Yves +4 · 7 citations
   #FOS: Computer and information sciences #FOS: Mathematics #Methodology (stat.ME) #Statistics Theory (math.ST)
3. On the choice of the low-dimensional domain for global optimization via random embeddings
   2017/04/18 by Binois, Mickaël, Ginsbourger, David, Roustant, Olivier · 3 citations
   #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Methodology (stat.ME) #Optimization and Control (math.OC)
4. Additive Kernels for Gaussian Process Modeling
   2011/03/21 by Nicolas Durrande, David Ginsbourger, Durrande, Nicolas +3 · 2 citations
   Computer Science · Decision Sciences · #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Probabilistic and Robust Engineering Design
5. Invariances of random fields paths, with applications in Gaussian Process Regression
   2013/08/06 by David Ginsbourger, Olivier Roustant, Ginsbourger, David +3 · 2 citations
   Computer Science · Decision Sciences · #Gaussian Processes and Bayesian Inference #Advanced Multi-Objective Optimization Algorithms #Probabilistic and Robust Engineering Design
6. A warped kernel improving robustness in Bayesian optimization via random embeddings
   2014/11/13 by Binois, Mickaël, Ginsbourger, David, Roustant, Olivier · 1 citation
   #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Optimization and Control (math.OC)
7. On ANOVA decompositions of kernels and Gaussian random field paths
   2014/09/21 by David Ginsbourger, Olivier Roustant, Ginsbourger, David +7 · 1 citation
   Computer Science · Decision Sciences · Physics and Astronomy · #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Model Reduction and Neural Networks #Probabilistic and Robust Engineering Design #Probability (math.PR) #Statistics Theory (math.ST)
8. Poincaré inequalities on intervals -- application to sensitivity analysis
   2016/12/12 by Roustant, Olivier, Barthe, Franck, Iooss, Bertrand · 1 citation
   #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Probability (math.PR) #Statistics Theory (math.ST)
9. A comparison of mixed-variables Bayesian optimization approaches
   2021/10/30 by Cuesta-Ramirez, Jhouben, Riche, Rodolphe Le, Roustant, Olivier +3 · 1 citation
   #Applications (stat.AP) #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
10. High-dimensional additive Gaussian processes under monotonicity constraints
    2022/05/17 by López-Lopera, Andrés F., Bachoc, François, Roustant, Olivier · 1 citation
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
11. On one dimensional weighted Poincare inequalities for Global Sensitivity Analysis
    2024/12/06 by Heredia, David, Joulin, Aldéric, Roustant, Olivier · 1 citation
    #FOS: Mathematics #Probability (math.PR) #Statistics Theory (math.ST)