15 citations · 28 across the 11 of their papers we have counts for
13 papers
Epistemic Uncertainty-Aware Defect Detection for Quality Control in Medical Device Manufacturing
Raham A. Butt, Marco Romanelli, Roche C. de Guzman
Objective: We investigate whether accounting for epistemic uncertainty can improve the reliability of automated defect detection in medical device manufacturing. Methods: We consid…
Happiness as a Measure of Fairness
Georg Pichler, Marco Romanelli, Pablo Piantanida
In this paper, we propose a novel fairness framework grounded in the concept of happiness, a measure of the utility each group gains fromdecisionoutcomes. Bycapturingfairness throu…
Beyond the Norms: Detecting Prediction Errors in Regression Models
Andres Altieri, Marco Romanelli, Georg Pichler +2
This paper tackles the challenge of detecting unreliable behavior in regression algorithms, which may arise from intrinsic variability (e.g., aleatoric uncertainty) or modeling err…
Optimal Zero-Shot Detector for Multi-Armed Attacks
Federica Granese, Marco Romanelli, Pablo Piantanida
This paper explores a scenario in which a malicious actor employs a multi-armed attack strategy to manipulate data samples, offering them various avenues to introduce noise into th…
INVICTUS: Optimizing Boolean Logic Circuit Synthesis via Synergistic Learning and Search
Animesh Basak Chowdhury, Marco Romanelli, Benjamin Tan +2
Logic synthesis is the first and most vital step in chip design. This steps converts a chip specification written in a hardware description language (such as Verilog) into an optim…
A Data-Driven Measure of Relative Uncertainty for Misclassification Detection
Eduardo Dadalto, Marco Romanelli, Georg Pichler +1
Misclassification detection is an important problem in machine learning, as it allows for the identification of instances where the model's predictions are unreliable. However, con…
A Minimax Approach Against Multi-Armed Adversarial Attacks Detection
Federica Granese, Marco Romanelli, Siddharth Garg +1
Multi-armed adversarial attacks, in which multiple algorithms and objective loss functions are simultaneously used at evaluation time, have been shown to be highly successful in fo…
MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors
Federica Granese, Marine Picot, Marco Romanelli +2
Detection of adversarial examples has been a hot topic in the last years due to its importance for safely deploying machine learning algorithms in critical applications. However, t…
Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning
Georg Pichler, Marco Romanelli, Leonardo Rey Vega +1
Federated Learning is expected to provide strong privacy guarantees, as only gradients or model parameters but no plain text training data is ever exchanged either between the clie…
DOCTOR: A Simple Method for Detecting Misclassification Errors
Federica Granese, Marco Romanelli, Daniele Gorla +2
Deep neural networks (DNNs) have shown to perform very well on large scale object recognition problems and lead to widespread use for real-world applications, including situations…
Estimating g-Leakage via Machine Learning
Marco Romanelli, Konstantinos Chatzikokolakis, Catuscia Palamidessi +1
This paper considers the problem of estimating the information leakage of a system in the black-box scenario. It is assumed that the system's internals are unknown to the learner,…
Feature selection in machine learning: Rényi min-entropy vs Shannon entropy
Catuscia Palamidessi, Marco Romanelli
Feature selection, in the context of machine learning, is the process of separating the highly predictive feature from those that might be irrelevant or redundant. Information theo…
Optimal Obfuscation Mechanisms via Machine Learning
Marco Romanelli, Konstantinos Chatzikokolakis, Catuscia Palamidessi
We consider the problem of obfuscating sensitive information while preserving utility, and we propose a machine learning approach inspired by the generative adversarial networks pa…