Leonard Berrada

22 papers A* 2Journal 16Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Privacy Awareness for Information-Sharing Assistants: A Case-study on Form-filling with Contextual Integrity. Trans. Mach. Learn. Res. Sahra Ghalebikesabi, Eugene Bagdasarian, Ren Yi, Itay Yona, Ilia Shumailov, Aneesh Pappu, Chongyang Shi, Laura Weidinger, Robert Stanforth, Leonard Berrada, Pushmeet Kohli, Po-Sen Huang, Borja Balle
2024	J	`jnl`	Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models. CoRR Soham De, Samuel L. Smith, Anushan Fernando, Aleksandar Botev, George-Cristian Muraru, Albert Gu, Ruba Haroun, Leonard Berrada, Yutian Chen, Srivatsan Srinivasan, Guillaume Desjardins, Arnaud Doucet, David Budden, Yee Whye Teh, Razvan Pascanu, Nando de Freitas, Caglar Gulcehre
2024	J	`jnl`	Operationalizing Contextual Integrity in Privacy-Conscious Assistants. CoRR Sahra Ghalebikesabi, Eugene Bagdasaryan, Ren Yi, Itay Yona, Ilia Shumailov, Aneesh Pappu, Chongyang Shi, Laura Weidinger, Robert Stanforth, Leonard Berrada, Pushmeet Kohli, Po-Sen Huang, Borja Balle
2024	J	`jnl`	RecurrentGemma: Moving Past Transformers for Efficient Open Language Models. CoRR Aleksandar Botev, Soham De, Samuel L. Smith, Anushan Fernando, George-Cristian Muraru, Ruba Haroun, Leonard Berrada, Razvan Pascanu, Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot, Johan Ferret, Sertan Girgin, Olivier Bachem, Alek Andreev, Kathleen Kenealy, Thomas Mesnard, Cassidy Hardin, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Armand Joulin, Noah Fiedel, Evan Senter, Yutian Chen, Srivatsan Srinivasan, Guillaume Desjardins, David Budden, Arnaud Doucet, Sharad Vikram, Adam Paszke, Trevor Gale, Sebastian Borgeaud, Charlie Chen, Andy Brock, Antonia Paterson, Jenny Brennan, Meg Risdal, Raj Gundluru, Nesh Devanathan, Paul Mooney, Nilay Chauhan, Phil Culliton, Luiz Gustavo Martins, Elisa Bandy, David Huntsperger, Glenn Cameron, Arthur Zucker, Tris Warkentin, Ludovic Peran, Minh Giang, Zoubin Ghahramani, Clément Farabet, Koray Kavukcuoglu, Demis Hassabis, Raia Hadsell, Yee Whye Teh, Nando de Frietas
2023	J	`jnl`	ConvNets Match Vision Transformers at Scale. CoRR Samuel L. Smith, Andrew Brock, Leonard Berrada, Soham De
2023	J	`jnl`	Differentially Private Diffusion Models Generate Useful Synthetic Images. CoRR Sahra Ghalebikesabi, Leonard Berrada, Sven Gowal, Ira Ktena, Robert Stanforth, Jamie Hayes, Soham De, Samuel L. Smith, Olivia Wiles, Borja Balle
2023	J	`jnl`	Unlocking Accuracy and Fairness in Differentially Private Image Classification. CoRR Leonard Berrada, Soham De, Judy Hanwen Shen, Jamie Hayes, Robert Stanforth, David Stutz, Pushmeet Kohli, Samuel L. Smith, Borja Balle
2022	J	`jnl`	A Stochastic Bundle Method for Interpolating Networks. CoRR Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar
2022	J	`jnl`	A Stochastic Bundle Method for Interpolation. J. Mach. Learn. Res. Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar
2022	J	`jnl`	Unlocking High-Accuracy Differentially Private Image Classification through Scale. CoRR Soham De, Leonard Berrada, Jamie Hayes, Samuel L. Smith, Borja Balle
2021	J	`jnl`	Comment on Stochastic Polyak Step-Size: Performance of ALI-G. CoRR Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
2021	A*	`conf`	Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications. NeurIPS Leonard Berrada, Sumanth Dathathri, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Jonathan Uesato, Sven Gowal, M. Pawan Kumar
2021	J	`jnl`	Verifying Probabilistic Specifications with Functional Lagrangians. CoRR Leonard Berrada, Sumanth Dathathri, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Jonathan Uesato, Sven Gowal, M. Pawan Kumar
2020	A*	`conf`	Training Neural Networks for and by Interpolation. ICML Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
2019	—	`conf`	Deep Frank-Wolfe For Neural Network Optimization. ICLR (Poster) Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
2019	—	`—`	Leveraging structure for optimization in deep learning. Leonard Berrada
2019	J	`jnl`	Training Neural Networks for and by Interpolation. CoRR Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
2018	J	`jnl`	Deep Frank-Wolfe For Neural Network Optimization. CoRR Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
2018	—	`conf`	Smooth Loss Functions for Deep Top-k Classification. ICLR (Poster) Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
2018	J	`jnl`	Smooth Loss Functions for Deep Top-k Classification. CoRR Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
2017	—	`conf`	Trusting SVM for Piecewise Linear CNNs. ICLR (Poster) Leonard Berrada, Andrew Zisserman, M. Pawan Kumar
2016	J	`jnl`	Trusting SVM for Piecewise Linear CNNs. CoRR Leonard Berrada, Andrew Zisserman, M. Pawan Kumar

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.