no code implementations • 28 Jul 2023 • Tiago Leon Melo, João Bravo, Marco O. P. Sampaio, Paolo Romano, Hugo Ferreira, João Tiago Ascensão, Pedro Bizarro
Adversarial attacks are a major concern in security-centered applications, where malicious actors continuously try to mislead Machine Learning (ML) models into wrongly classifying fraudulent activity as legitimate, whereas system maintainers try to stop them.
1 code implementation • 5 Apr 2023 • Pedro Mendes, Paolo Romano, David Garlan
This work focuses on the problem of hyper-parameter tuning (HPT) for robust (i. e., adversarially trained) models, shedding light on the new challenges and opportunities arising during the HPT process for robust models.
1 code implementation • 5 Aug 2021 • Pedro Mendes, Maria Casimiro, Paolo Romano, David Garlan
In the literature on hyper-parameter tuning, a number of recent solutions rely on low-fidelity observations (e. g., training with sub-sampled datasets) in order to efficiently identify promising configurations to be then tested via high-fidelity observations (e. g., using the full dataset).
no code implementations • 9 Nov 2020 • Pedro Mendes, Maria Casimiro, Paolo Romano, David Garlan
This work introduces TrimTuner, the first system for optimizing machine learning jobs in the cloud to exploit sub-sampling techniques to reduce the cost of the optimization process while keeping into account user-specified constraints.
2 code implementations • 6 Mar 2020 • David Gureya, João Neto, Reza Karimi, João Barreto, Pramod Bhatotia, Vivien Quema, Rodrigo Rodrigues, Paolo Romano, Vladimir Vlassov
Page placement is a critical problem for memoryintensive applications running on a shared-memory multiprocessor with a non-uniform memory access (NUMA) architecture.
Distributed, Parallel, and Cluster Computing
no code implementations • 19 Oct 2014 • Diego Didona, Paolo Romano
Performance modeling typically relies on two antithetic methodologies: white box models, which exploit knowledge on system's internals and capture its dynamics using analytical approaches, and black box techniques, which infer relations among the input and output variables of a system based on the evidences gathered during an initial training phase.