GPareto: An R Package for Gaussian-Process-Based Multi-Objective Optimization and Analysis

Autor: Mickaël Binois, Victor Picheny

Publikováno v: Journal of Statistical Software, Vol 89, Iss 1, Pp 1-30 (2019)

The GPareto package for R provides multi-objective optimization algorithms for expensive black-box functions and an ensemble of dedicated uncertainty quantification methods. Popular methods such as efficient global optimization in the mono-objective

Externí odkaz: https://doaj.org/article/de78bb3a92074a08859ac1e2d0753b96

A framework for allocating conservation resources among multiple threats and actions

Autor: Victor Picheny, Alana L. Moore, Cindy E. Hauser, Libby Rumpff, Michael C. Runge, Abbey E. Camaclang, Joslin L. Moore

Publikováno v: Conservation Biology. 35:1639-1649

Land managers decide how to allocate resources among multiple threats that can be addressed through multiple possible actions. Additionally, these actions vary in feasibility, effectiveness, and cost. We sought to provide a way to optimize resource a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::006e3e7ce1316fd81e2e0550a0c06721
https://doi.org/10.1111/cobi.13748

Fantasizing with Dual GPs in Bayesian Optimization and Active Learning

Autor: Paul Chang, Prakhar Verma, Ti John, Victor Picheny, Henry Moss, Arno Solin

Publikováno v: Aalto University

Gaussian processes (GPs) are the main surrogate functions used for sequential modelling such as Bayesian Optimization and Active Learning. Their drawbacks are poor scaling with data and the need to run an optimization loop when using a non-Gaussian l

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dad744981b0ee330746fa3a7802f4ebe

Revisiting Bayesian Optimization in the light of the COCO benchmark

Autor: Victor Picheny, Rodolphe Le Riche

Publikováno v: Structural and Multidisciplinary Optimization
Structural and Multidisciplinary Optimization, 2021, ⟨10.1007/s00158-021-02977-1⟩
Structural and Multidisciplinary Optimization, Springer Verlag (Germany), 2021, ⟨10.1007/s00158-021-02977-1⟩

International audience; It is commonly believed that Bayesian optimization (BO) algorithms are highly efficient for optimizing numerically costly functions. However, BO is not often compared to widely different alternatives, and is mostly tested on n

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::32b45fb6a47ce1ef8147d4148bd0e248
https://hal.science/hal-03188590

Modeling and Optimization with Gaussian Processes in Reduced Eigenbases

Autor: David Gaudrie, Benoit Enaux, Rodolphe Le Riche, Victor Picheny, Vincent Herbert

Publikováno v: Structural and Multidisciplinary Optimization
Structural and Multidisciplinary Optimization, 2020, 61, pp.2343-2361. ⟨10.1007/s00158-019-02458-6⟩
Structural and Multidisciplinary Optimization, Springer Verlag (Germany), 2020, 61, pp.2343-2361. ⟨10.1007/s00158-019-02458-6⟩
Structural and Multidisciplinary Optimization, Springer Verlag (Germany), 2019

International audience; Parametric shape optimization aims at minimizing an objective function f(x) where x are CAD parameters. This task is difficult when f is the output of an expensive-to-evaluate numerical simulator and the number of CAD paramete

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39c163582fc96512046dbe45460f0420
https://hal-emse.ccsd.cnrs.fr/emse-02390627