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Found 6 papers, 3 papers with code
Adversarial training for tabular data with attack propagation
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.
Hyper-parameter Tuning for Adversarially Robust Models
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.
HyperJump: Accelerating HyperBand via Risk Modelling
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).
TrimTuner: Efficient Optimization of Machine Learning Jobs in the Cloud via Sub-Sampling
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.
Bandwidth-Aware Page Placement in NUMA
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
On Bootstrapping Machine Learning Performance Predictors via Analytical Models
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.
