Zobrazeno 1 - 10
of 21
pro vyhledávání: '"MARTINELLI, JULIEN"'
Preferential Bayesian optimization (PBO) is a sample-efficient framework for learning human preferences between candidate designs. PBO classically relies on homoscedastic noise models to represent human aleatoric uncertainty. Yet, such noise fails to
Externí odkaz:
http://arxiv.org/abs/2405.14657
In many settings, such as scientific inference, optimization, and transfer learning, the learner has a well-defined objective, which can be treated as estimation of a target parameter, and no intrinsic interest in characterizing the entire data-gener
Externí odkaz:
http://arxiv.org/abs/2310.14968
Autor:
Martinelli, Julien, Bharti, Ayush, Tiihonen, Armi, John, S. T., Filstroff, Louis, Sloman, Sabina J., Rinke, Patrick, Kaski, Samuel
Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding the environment, such as experimental conditions. However, the releva
Externí odkaz:
http://arxiv.org/abs/2305.14120
Bayesian optimization (BO) is a powerful framework for optimizing black-box, expensive-to-evaluate functions. Over the past decade, many algorithms have been proposed to integrate cheaper, lower-fidelity approximations of the objective function into
Externí odkaz:
http://arxiv.org/abs/2210.13937
Inferring chemical reaction networks (CRN) from concentration time series is a challenge encouragedby the growing availability of quantitative temporal data at the cellular level. This motivates thedesign of algorithms to infer the preponderant react
Externí odkaz:
http://arxiv.org/abs/2209.03185
Autor:
Frioux Clémence, Huet Sylvie, Labarthe Simon, Martinelli Julien, Malou Thibault, Sherman David, Taupin Marie-Luce, Ugalde-Salas Pablo
Publikováno v:
ESAIM: Proceedings and Surveys, Vol 73, Pp 187-217 (2023)
Mathematical and numerical models are increasingly used in microbial ecology to model the fate of microbial communities in their ecosystem. These models allow to connect in a mechanistic framework species-level informations, such as the microbial gen
Externí odkaz:
https://doaj.org/article/4b33d1c53b4047db89663589b190ab8b
Bayesian optimization (BO) is a powerful framework for optimizing black-box, expensive-to-evaluate functions. Over the past decade, many algorithms have been proposed to integrate cheaper, lower-fidelity approximations of the objective function into
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f771b142a374cb8d3625c9d3e636c5f0
https://aaltodoc.aalto.fi/handle/123456789/120533
https://aaltodoc.aalto.fi/handle/123456789/120533
Inferring chemical reaction networks (CRN) from concentration time series is a challenge encouragedby the growing availability of quantitative temporal data at the cellular level. This motivates thedesign of algorithms to infer the preponderant react
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b5d9f3d93403d63024bdeaa94f076be
https://hal.science/hal-03769872v2/file/papier.pdf
https://hal.science/hal-03769872v2/file/papier.pdf
Autor:
Martinelli, Julien, Bharti, Ayush, John, S. T., Tiihonen, Armi, Sloman, Sabina, Filstroff, Louis, Kaski, Samuel
Contextual Bayesian Optimization (CBO) is a powerful framework for optimizing black-box, expensive-to-evaluate functions with respect to design variables, while simultaneously efficiently integrating relevant contextual information regarding the envi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59ba17c0e960fe9bd3ffd301adac8937
Inferring chemical reaction networks (CRN) from time series data is a challenge encouraged by the growing availability of quantitative temporal data at the cellular level. This motivates the design of algorithms to infer the preponderant reactions be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1398::0992ff8c9c0d3383a3ca5b042303d1e2
https://hal.archives-ouvertes.fr/hal-03769872/file/mgfs21bioinformatics.pdf
https://hal.archives-ouvertes.fr/hal-03769872/file/mgfs21bioinformatics.pdf