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pro vyhledávání: '"Díaz, Mario"'
Local differential privacy (LDP) is increasingly employed in privacy-preserving machine learning to protect user data before sharing it with an untrusted aggregator. Most LDP methods assume that users possess only a single data record, which is a sig
Externí odkaz:
http://arxiv.org/abs/2411.08791
The nonparametric view of Bayesian inference has transformed statistics and many of its applications. The canonical Dirichlet process and other more general families of nonparametric priors have served as a gateway to solve frontier uncertainty quant
Externí odkaz:
http://arxiv.org/abs/2308.11868
Autor:
Asoodeh, Shahab, Diaz, Mario
The Noisy-SGD algorithm is widely used for privately training machine learning models. Traditional privacy analyses of this algorithm assume that the internal state is publicly revealed, resulting in privacy loss bounds that increase indefinitely wit
Externí odkaz:
http://arxiv.org/abs/2305.09903
Bioconversion of carboxylic acids derived from Kraft black liquor into lipids by Yarrowia lipolytica
Publikováno v:
In Journal of Cleaner Production 15 November 2024 480
Publikováno v:
In Journal of Water Process Engineering November 2024 67
Publikováno v:
In Journal of Environmental Chemical Engineering October 2024 12(5)
Autor:
Ridella, Florencia, Carpintero, María, Marcet, Ismael, Matos, María, Gutiérrez, Gemma, Rendueles, Manuel, Díaz, Mario
Publikováno v:
In Carbohydrate Polymers 15 September 2024 340
Autor:
Núñez, Daniel, Zabatta, Martina, Oulego, Paula, Collado, Sergio, Riera, Francisco A., Díaz, Mario
Publikováno v:
In Separation and Purification Technology 6 September 2024 343