Federica Gerace

20 papers A* 4Journal 15

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Random Matrix Theory of Masked Self-Supervised Regression. CoRR Arie Wortsman Zurich, Federica Gerace, Bruno Loureiro, Yue M. Lu
2025	J	`jnl`	Generalization performance of narrow one-hidden layer networks in the teacher-student setting. CoRR Jean Barbier, Federica Gerace, Alessandro Ingrosso, Clarissa Lauditi, Enrico M. Malatesta, Gibbs Nwemadji, Rodrigo Pérez Ortiz
2025	J	`jnl`	Testing Transformer Learnability on the Arithmetic Sequence of Rooted Trees. CoRR Alessandro Breccia, Federica Gerace, Marco Lippi, Gabriele Sicuro, Pierluigi Contucci
2024	A*	`conf`	A distributional simplicity bias in the learning dynamics of transformers. NeurIPS Riccardo Rende, Federica Gerace, Alessandro Laio, Sebastian Goldt
2024	J	`jnl`	A distributional simplicity bias in the learning dynamics of transformers. CoRR Riccardo Rende, Federica Gerace, Alessandro Laio, Sebastian Goldt
2024	J	`jnl`	How to choose the right transfer learning protocol? A qualitative analysis in a controlled set-up. Trans. Mach. Learn. Res. Federica Gerace, Diego Doimo, Stefano Sarao Mannelli, Luca Saglietti, Alessandro Laio
2024	A*	`conf`	Learning from higher-order correlations, efficiently: hypothesis tests, random features, and neural networks. NeurIPS Eszter Székely, Lorenzo Bardone, Federica Gerace, Sebastian Goldt
2023	J	`jnl`	Learning from higher-order statistics, efficiently: hypothesis tests, random features, and neural networks. CoRR Eszter Székely, Lorenzo Bardone, Federica Gerace, Sebastian Goldt
2023	J	`jnl`	Optimal inference of a generalised Potts model by single-layer transformers with factored attention. CoRR Riccardo Rende, Federica Gerace, Alessandro Laio, Sebastian Goldt
2023	J	`jnl`	Optimal transfer protocol by incremental layer defrosting. CoRR Federica Gerace, Diego Doimo, Stefano Sarao Mannelli, Luca Saglietti, Alessandro Laio
2022	J	`jnl`	Gaussian Universality of Linear Classifiers with Random Labels in High-Dimension. CoRR Federica Gerace, Florent Krzakala, Bruno Loureiro, Ludovic Stephan, Lenka Zdeborová
2022	J	`jnl`	Inducing bias is simpler than you think. CoRR Stefano Sarao Mannelli, Federica Gerace, Negar Rostamzadeh, Luca Saglietti
2022	J	`jnl`	Probing transfer learning with a model of synthetic correlated datasets. Mach. Learn. Sci. Technol. Federica Gerace, Luca Saglietti, Stefano Sarao Mannelli, Andrew M. Saxe, Lenka Zdeborová
2021	J	`jnl`	Probing transfer learning with a model of synthetic correlated datasets. CoRR Federica Gerace, Luca Saglietti, Stefano Sarao Mannelli, Andrew M. Saxe, Lenka Zdeborová
2020	A*	`conf`	Critical initialisation in continuous approximations of binary neural networks. ICLR George Stamatescu, Federica Gerace, Carlo Lucibello, Ian G. Fuss, Langford B. White
2020	A*	`conf`	Generalisation error in learning with random features and the hidden manifold model. ICML Federica Gerace, Bruno Loureiro, Florent Krzakala, Marc Mézard, Lenka Zdeborová
2020	J	`jnl`	Generalisation error in learning with random features and the hidden manifold model. CoRR Federica Gerace, Bruno Loureiro, Florent Krzakala, Marc Mézard, Lenka Zdeborová
2019	J	`jnl`	Signal propagation in continuous approximations of binary neural networks. CoRR George Stamatescu, Federica Gerace, Carlo Lucibello, Ian G. Fuss, Langford B. White
2018	—	`—`	Statistical physics of neural systems. Federica Gerace
2017	J	`jnl`	On the role of synaptic stochasticity in training low-precision neural networks. CoRR Carlo Baldassi, Federica Gerace, Hilbert J. Kappen, Carlo Lucibello, Luca Saglietti, Enzo Tartaglione, Riccardo Zecchina

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.