15 citations · 28 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…