Omar Montasser

38 papers A* 14A 2Misc 1Journal 21

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	(Im)possibility of Automated Hallucination Detection in Large Language Models. CoRR Amin Karbasi, Omar Montasser, John Sous, Grigoris Velegkas
2025	A*	`conf`	Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks. COLT Omar Montasser, Abhishek Shetty, Nikita Zhivotovskiy
2025	J	`jnl`	Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks. CoRR Omar Montasser, Abhishek Shetty, Nikita Zhivotovskiy
2025	J	`jnl`	CoT Information: Improved Sample Complexity under Chain-of-Thought Supervision. CoRR Awni Altabaa, Omar Montasser, John Lafferty
2025	J	`jnl`	DDPM Score Matching and Distribution Learning. CoRR Sinho Chewi, Alkis Kalavasis, Anay Mehrotra, Omar Montasser
2025	J	`jnl`	Sample-Adaptivity Tradeoff in On-Demand Sampling. CoRR Nika Haghtalab, Omar Montasser, Mingda Qiao
2024	A	`conf`	Agnostic Multi-Robust Learning using ERM. AISTATS Saba Ahmadi, Avrim Blum, Omar Montasser, Kevin M. Stangl
2024	A*	`conf`	Derandomizing Multi-Distribution Learning. NeurIPS Kasper Green Larsen, Omar Montasser, Nikita Zhivotovskiy
2024	J	`jnl`	Derandomizing Multi-Distribution Learning. CoRR Kasper Green Larsen, Omar Montasser, Nikita Zhivotovskiy
2024	A*	`conf`	Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization. NeurIPS Omar Montasser, Han Shao, Emmanuel Abbe
2024	J	`jnl`	Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization. CoRR Omar Montasser, Han Shao, Emmanuel Abbe
2023	J	`jnl`	Certifiable (Multi)Robustness Against Patch Attacks Using ERM. CoRR Saba Ahmadi, Avrim Blum, Omar Montasser, Kevin Stangl
2023	A*	`conf`	Strategic Classification under Unknown Personalized Manipulation. NeurIPS Han Shao, Avrim Blum, Omar Montasser
2023	J	`jnl`	Strategic Classification under Unknown Personalized Manipulation. CoRR Han Shao, Avrim Blum, Omar Montasser
2023	J	`jnl`	Theoretical Foundations of Adversarially Robust Learning. CoRR Omar Montasser
2022	A*	`conf`	A Theory of PAC Learnability under Transformation Invariances. NeurIPS Han Shao, Omar Montasser, Avrim Blum
2022	J	`jnl`	A Theory of PAC Learnability under Transformation Invariances. CoRR Han Shao, Omar Montasser, Avrim Blum
2022	A*	`conf`	Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization. NeurIPS Omar Montasser, Steve Hanneke, Nati Srebro
2022	J	`jnl`	Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization. CoRR Omar Montasser, Steve Hanneke, Nathan Srebro
2022	A*	`conf`	Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness. NeurIPS Avrim Blum, Omar Montasser, Greg Shakhnarovich, Hongyang Zhang
2022	J	`jnl`	Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness. CoRR Avrim Blum, Omar Montasser, Greg Shakhnarovich, Hongyang Zhang
2022	A	`conf`	Transductive Robust Learning Guarantees. AISTATS Omar Montasser, Steve Hanneke, Nathan Srebro
2021	A*	`conf`	Adversarially Robust Learning with Unknown Perturbation Sets. COLT Omar Montasser, Steve Hanneke, Nathan Srebro
2021	J	`jnl`	Adversarially Robust Learning with Unknown Perturbation Sets. CoRR Omar Montasser, Steve Hanneke, Nathan Srebro
2021	J	`jnl`	Transductive Robust Learning Guarantees. CoRR Omar Montasser, Steve Hanneke, Nathan Srebro
2020	A*	`conf`	Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity. COLT Pritish Kamath, Omar Montasser, Nathan Srebro
2020	J	`jnl`	Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity. CoRR Pritish Kamath, Omar Montasser, Nathan Srebro
2020	A*	`conf`	Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples. NeurIPS Shafi Goldwasser, Adam Tauman Kalai, Yael Kalai, Omar Montasser
2020	J	`jnl`	Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples. CoRR Shafi Goldwasser, Adam Tauman Kalai, Yael Tauman Kalai, Omar Montasser
2020	A*	`conf`	Efficiently Learning Adversarially Robust Halfspaces with Noise. ICML Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro
2020	J	`jnl`	Efficiently Learning Adversarially Robust Halfspaces with Noise. CoRR Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro
2020	Misc	`conf`	Identifying unpredictable test examples with worst-case guarantees. ITA Shafi Goldwasser, Adam Tauman Kalai, Yael Tauman Kalai, Omar Montasser
2020	A*	`conf`	Reducing Adversarially Robust Learning to Non-Robust PAC Learning. NeurIPS Omar Montasser, Steve Hanneke, Nati Srebro
2020	J	`jnl`	Reducing Adversarially Robust Learning to Non-Robust PAC Learning. CoRR Omar Montasser, Steve Hanneke, Nathan Srebro
2019	A*	`conf`	VC Classes are Adversarially Robustly Learnable, but Only Improperly. COLT Omar Montasser, Steve Hanneke, Nathan Srebro
2019	J	`jnl`	VC Classes are Adversarially Robustly Learnable, but Only Improperly. CoRR Omar Montasser, Steve Hanneke, Nathan Srebro
2017	A*	`conf`	Predicting Demographics of High-Resolution Geographies with Geotagged Tweets. AAAI Omar Montasser, Daniel Kifer
2017	J	`jnl`	Predicting Demographics of High-Resolution Geographies with Geotagged Tweets. CoRR Omar Montasser, Daniel Kifer

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.