Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Pinzon, Carlos"'
Autor:
Pinzón, Carlos
Among all metrics based on p-norms, the Manhattan (p=1), euclidean (p=2) and Chebyshev distances (p=infinity) are the most widely used for their interpretability, simplicity and technical convenience. But these are not the only arguments for the ubiq
Externí odkaz:
http://arxiv.org/abs/2411.13567
Autor:
Binkytė, Rūta, Pinzón, Carlos, Lestyán, Szilvia, Jung, Kangsoo, Arcolezi, Héber H., Palamidessi, Catuscia
Differential privacy is a widely adopted framework designed to safeguard the sensitive information of data providers within a data set. It is based on the application of controlled noise at the interface between the server that stores and processes t
Externí odkaz:
http://arxiv.org/abs/2311.04037
Structures involving a lattice and join-endomorphisms on it are ubiquitous in computer science. We study the cardinality of the set $\mathcal{E}(L)$ of all join-endomorphisms of a given finite lattice $L$. In particular, we show for $\mathbf{M}_n$, t
Externí odkaz:
http://arxiv.org/abs/2211.00781
Let $L$ be a finite lattice and $\mathcal{E}(L)$ be the set of join endomorphisms of $L$. We consider the problem of given $L$ and $f,g \in \mathcal{E}(L)$, finding the greatest lower bound $f \sqcap_{{\scriptsize \mathcal{E}(L)}} g$ in the lattice $
Externí odkaz:
http://arxiv.org/abs/2210.08128
Collecting and analyzing evolving longitudinal data has become a common practice. One possible approach to protect the users' privacy in this context is to use local differential privacy (LDP) protocols, which ensure the privacy protection of all use
Externí odkaz:
http://arxiv.org/abs/2210.00262
An attacker can gain information of a user by analyzing its network traffic. The size of transferred data leaks information about the file being transferred or the service being used, and this is particularly revealing when the attacker has backgroun
Externí odkaz:
http://arxiv.org/abs/2209.04379
Autor:
Binkytė-Sadauskienė, Rūta, Makhlouf, Karima, Pinzón, Carlos, Zhioua, Sami, Palamidessi, Catuscia
It is crucial to consider the social and ethical consequences of AI and ML based decisions for the safe and acceptable use of these emerging technologies. Fairness, in particular, guarantees that the ML decisions do not result in discrimination again
Externí odkaz:
http://arxiv.org/abs/2206.06685
One of the main concerns about fairness in machine learning (ML) is that, in order to achieve it, one may have to trade off some accuracy. To overcome this issue, Hardt et al. proposed the notion of equality of opportunity (EO), which is compatible w
Externí odkaz:
http://arxiv.org/abs/2107.06944
Autor:
Salazar-Caceres, Fabian, Ramirez-Murillo, Harrynson, Torres-Pinzón, Carlos Andrés, Camargo-Martínez, Martha Patricia
Publikováno v:
In Alexandria Engineering Journal January 2024 87:175-185
This paper proposes a random network model for blockchains, a distributed hierarchical data structure of blocks that has found several applications in various industries. The model is parametric on two probability distribution functions governing blo
Externí odkaz:
http://arxiv.org/abs/1909.06435