229 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Université Fédérale de Toulouse Midi-PyrénéesFR19 papers
- Centre National de la Recherche ScientifiqueFR17 papers
- Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO)FR15 papers
- Université Toulouse III - Paul SabatierFR8 papers
- École Nationale de l’Aviation CivileFR6 papers
- Office National d'Études et de Recherches AérospatialesFR5 papers
- Université Paris-SaclayFR5 papers
- Institut Clément AderFR4 papers
- Institut de Recherche en Astrophysique et PlanétologieFR4 papers
- Polytechnique MontréalCA4 papers
- Université de ToulouseFR4 papers
- Astrophysique, Instrumentation et ModélisationFR3 papers
4 papers · 1 filter
Modeling Hierarchical Spaces: A Review and Unified Framework for Surrogate-Based Architecture Design
Paul Saves, Edward Hallé-Hannan, Jasper Bussemaker +2
Simulation-based problems involving mixed-variable inputs frequently feature domains that are hierarchical, conditional, heterogeneous, or tree-structured. These characteristics po…
SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes
Paul Saves, Remi Lafage, Nathalie Bartoli +6
The Surrogate Modeling Toolbox (SMT) is an open-source Python package that offers a collection of surrogate modeling methods, sampling techniques, and a set of sample problems. Thi…
An Offline Risk-aware Policy Selection Method for Bayesian Markov Decision Processes
Giorgio Angelotti, Nicolas Drougard, Caroline Ponzoni Carvalho Chanel
In Offline Model Learning for Planning and in Offline Reinforcement Learning, the limited data set hinders the estimate of the Value function of the relative Markov Decision Proces…
Open Loop Execution of Tree-Search Algorithms, extended version
Erwan Lecarpentier, Guillaume Infantes, Charles Lesire +1
In the context of tree-search stochastic planning algorithms where a generative model is available, we consider on-line planning algorithms building trees in order to recommend an…