Benjamin Aubin

21 papers A* 4Journal 14Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Fast, faithful and photorealistic diffusion-based image super-resolution with enhanced Flow Map models. CoRR Maxence Noble, Gonzalo Iñaki Quintana, Benjamin Aubin, Clément Chadebec
2025	A*	`conf`	Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation. AAAI Clément Chadebec, Onur Tasar, Eyal Benaroche, Benjamin Aubin
2025	J	`jnl`	LBM: Latent Bridge Matching for Fast Image-to-Image Translation. CoRR Clément Chadebec, Onur Tasar, Sanjeev Sreetharan, Benjamin Aubin
2024	J	`jnl`	Controllable Shadow Generation with Single-Step Diffusion Models from Synthetic Data. CoRR Onur Tasar, Clément Chadebec, Benjamin Aubin
2024	J	`jnl`	Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation. CoRR Clément Chadebec, Onur Tasar, Eyal Benaroche, Benjamin Aubin
2023	J	`jnl`	Tree-AMP: Compositional Inference with Tree Approximate Message Passing. J. Mach. Learn. Res. Antoine Baker, Florent Krzakala, Benjamin Aubin, Lenka Zdeborová
2021	J	`jnl`	Linear unit-tests for invariance discovery. CoRR Benjamin Aubin, Agnieszka Slowik, Martín Arjovsky, Léon Bottou, David Lopez-Paz
2021	J	`jnl`	Mean-field methods and algorithmic perspectives for high-dimensional machine learning. CoRR Benjamin Aubin
2021	J	`jnl`	The Spiked Matrix Model With Generative Priors. IEEE Trans. Inf. Theory Benjamin Aubin, Bruno Loureiro, Antoine Maillard, Florent Krzakala, Lenka Zdeborová
2020	—	`conf`	Exact asymptotics for phase retrieval and compressed sensing with random generative priors. MSML Benjamin Aubin, Bruno Loureiro, Antoine Baker, Florent Krzakala, Lenka Zdeborová
2020	A*	`conf`	Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization. NeurIPS Benjamin Aubin, Florent Krzakala, Yue M. Lu, Lenka Zdeborová
2020	J	`jnl`	Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization. CoRR Benjamin Aubin, Florent Krzakala, Yue M. Lu, Lenka Zdeborová
2020	—	`—`	Mean-field methods and algorithmic perspectives for high-dimensional machine learning. (Méthodes à champ moyen et perspectives algorithmiques pour l'apprentissage automatique en haute dimension). Benjamin Aubin
2020	—	`conf`	Rademacher complexity and spin glasses: A link between the replica and statistical theories of learning. MSML Alia Abbara, Benjamin Aubin, Florent Krzakala, Lenka Zdeborová
2020	J	`jnl`	TRAMP: Compositional Inference with TRee Approximate Message Passing. CoRR Antoine Baker, Benjamin Aubin, Florent Krzakala, Lenka Zdeborová
2019	J	`jnl`	Exact asymptotics for phase retrieval and compressed sensing with random generative priors. CoRR Benjamin Aubin, Bruno Loureiro, Antoine Baker, Florent Krzakala, Lenka Zdeborová
2019	J	`jnl`	Rademacher complexity and spin glasses: A link between the replica and statistical theories of learning. CoRR Alia Abbara, Benjamin Aubin, Florent Krzakala, Lenka Zdeborová
2019	A*	`conf`	The spiked matrix model with generative priors. NeurIPS Benjamin Aubin, Bruno Loureiro, Antoine Maillard, Florent Krzakala, Lenka Zdeborová
2019	J	`jnl`	The spiked matrix model with generative priors. CoRR Benjamin Aubin, Bruno Loureiro, Antoine Maillard, Florent Krzakala, Lenka Zdeborová
2018	A*	`conf`	The committee machine: Computational to statistical gaps in learning a two-layers neural network. NeurIPS Benjamin Aubin, Antoine Maillard, Jean Barbier, Florent Krzakala, Nicolas Macris, Lenka Zdeborová
2018	J	`jnl`	The committee machine: Computational to statistical gaps in learning a two-layers neural network. CoRR Benjamin Aubin, Antoine Maillard, Jean Barbier, Florent Krzakala, Nicolas Macris, Lenka Zdeborová

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.