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Federico Siciliano

Hi! I’m Federico Siciliano, a PhD Student in Data Science at Sapienza University of Rome. My research interests include Explainable Artificial Intelligence, Foundations of Deep Learning and Information Retrieval. I’m part of the RSTLess research group, which focuses on Robust, Safe and Transparent Deep Learning. Prior to this, I completed my Master’s Degree in Data Science at Sapienza University of Rome.

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The Power of Noise: Redefining Retrieval for RAG Systems
Cuconasu, Florin; Trappolini, Giovanni; Siciliano, Federico; Filice, Simone; Campagnano, Cesare; Maarek, Yoelle; Tonellotto, Nicola; Silvestri, Fabrizio
2024
Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels
Bucarelli, Maria Sofia; Cassano, Lucas; Siciliano, Federico; Mantrach, Amin; Silvestri, Fabrizio;
2023
Deep active learning for misinformation detection using geometric deep learning
Barnabò, Giorgio; Siciliano, Federico; Castillo, Carlos; Leonardi, Stefano; Nakov, Preslav; Da San Martino, Giovanni; Silvestri, Fabrizio;
2023
A data-driven approach to refine predictions of differentiated thyroid cancer outcomes: a prospective multicenter study
Grani, Giorgio; Gentili, Michele; Siciliano, Federico; Albano, Domenico; Zilioli, Valentina; Morelli, Silvia; Puxeddu, Efisio; Zatelli, Maria Chiara; Gagliardi, Irene; Piovesan, Alessandro;
2023
Integrating Item Relevance in Training Loss for Sequential Recommender Systems
Bacciu, Andrea; Siciliano, Federico; Tonellotto, Nicola; Silvestri, Fabrizio;
2023
RRAML: Reinforced Retrieval Augmented Machine Learning
Bacciu, Andrea; Cuconasu, Florin; Siciliano, Federico; Silvestri, Fabrizio; Tonellotto, Nicola; Trappolini, Giovanni;
2023
Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbations: an Empirical Study
Betello, Filippo; Siciliano, Federico; Mishra, Pushkar; Silvestri, Fabrizio;
2023
Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It
Federico Siciliano, Luca Maiano, Lorenzo Papa, Irene Amerini, Fabrizio Silvestri
2023
Adata-driven approach reveals emerging risk factors for recurrent and persistent differentiated thyroid cancer
Gentili, Michele; Grani, Giorgio; Siciliano, Federico;
2022
Comparing long short-term memory and convolutional neural networks in SYM-H index forecasting
Siciliano, Federico; Consolini, Giuseppe; Giannattasio, Fabio;
2022
Newron: a new generalization of the artificial neuron to enhance the interpretability of neural networks
Siciliano, Federico; Bucarelli, Maria Sofia; Tolomei, Gabriele; Silvestri, Fabrizio;
2022
FbMultiLingMisinfo: Challenging Large-Scale Multilingual Benchmark for Misinformation Detection
Barnabò, Giorgio; Siciliano, Federico; Castillo, Carlos; Leonardi, Stefano; Nakov, Preslav; Da San Martino, Giovanni; Silvestri, Fabrizio;
2022
Encoding Concepts in Graph Neural Networks
Magister, Lucie Charlotte; Barbiero, Pietro; Kazhdan, Dmitry; Siciliano, Federico; Ciravegna, Gabriele; Silvestri, Fabrizio; Jamnik, Mateja; Lio, Pietro;
2022
Leveraging Deep Learning models to assess the temporal validity of Emotional Text Mining procedures
Greco, F; Polli, A; Siciliano, F;
2022
Forecasting SYM‐H Index: A Comparison Between Long Short‐Term Memory and Convolutional Neural Networks
Siciliano, F; Consolini, Giuseppe; Tozzi, Roberta; Gentili, M; Giannattasio, Fabio; De Michelis, Paola;
2021
Reconstruction of F-Region Electric Current Densities from more than 2 Years of Swarm Satellite Magnetic data
Tozzi, Roberta; Pezzopane, Michael; De Michelis, Paola; Pignalberi, Alessio; Siciliano, Federico;
2016

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