12 citations · 13 across the 13 of their papers we have counts for
15 papers
Smaller Models, Unexpected Costs: Trade-offs in LLM Quantization for Automated Program Repair
Fernando Vallecillos-Ruiz, Giordano d'Aloisio, Max Hort +3
Large Language Models (LLMs) are powerful tools and have been increasingly adopted for complex software engineering tasks. As the number of parameters increases, results can often…
SafeTune: Search-based Harmfulness Minimisation for Large Language Models
Giordano d'Aloisio, David Williams, Giusy Annunziata +3
The widespread adoption of Large Language Models (LLMs) raises concerns about the potential harmfulness of their responses. In this paper, we first investigate the harmfulness of r…
A Generalised Exponentiated Gradient Approach to Enhance Fairness in Binary and Multi-class Classification Tasks
Maryam Boubekraoui, Giordano d'Aloisio, Antinisca Di Marco
The widespread use of AI and ML models in sensitive areas raises significant concerns about fairness. While the research community has introduced various methods for bias mitigatio…
A Data-Driven Approach to Support Clinical Renal Replacement Therapy
Alice Balboni, Luis Escobar, Andrea Manno +5
This study investigates a data-driven machine learning approach to predict membrane fouling in critically ill patients undergoing Continuous Renal Replacement Therapy (CRRT). Using…
FairRF: Multi-Objective Search for Single and Intersectional Software Fairness
Giordano d'Alosio, Max Hort, Rebecca Moussa +1
Background: The wide adoption of AI- and ML-based systems in sensitive domains raises severe concerns about their fairness. Many methods have been proposed in the literature to enh…
REPAIR Approach for Social-based City Reconstruction Planning in case of natural disasters
Ghulam Mudassir, Antinisca Di Marco, Giordano d'Aloisio
Natural disasters always have several effects on human lives. It is challenging for governments to tackle these incidents and to rebuild the economic, social and physical infrastru…