Edward Moroshko

32 papers A* 7A 2B 1Journal 21Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Causal Framework for Mitigating Data Shifts in Healthcare. CoRR Kurt Butler, Stephanie Riley, Damian Machlanski, Edward Moroshko, Panagiotis Dimitrakopoulos, Thomas Melistas, Akchunya Chanchal, Konstantinos Vilouras, Zhihua Liu, Steven McDonagh, Hana Chockler, Ben Glocker, Niccoló Tempini, Matthew Sperrin, Sotirios A. Tsaftaris, Ricardo Silva
2026	J	`jnl`	Optimal L2 Regularization in High-dimensional Continual Linear Regression. CoRR Gilad Karpel, Edward Moroshko, Ran Levinstein, Ron Meir, Daniel Soudry, Itay Evron
2025	J	`jnl`	A Shift in Perspective on Causality in Domain Generalization. CoRR Damian Machlanski, Stephanie Riley, Edward Moroshko, Kurt Butler, Panagiotis Dimitrakopoulos, Thomas Melistas, Akchunya Chanchal, Steven McDonagh, Ricardo Silva, Sotirios A. Tsaftaris
2025	J	`jnl`	A model predictive control framework with customer-priority tiers for virtual power plant resilience during extreme weather: A UK heatwave case study. CoRR Edward Moroshko, Weizhe Qin, Desen Kirli, Mohammed Qais, Sotirios A. Tsaftaris, Aristides E. Kiprakis
2025	J	`jnl`	CRCE: Coreference-Retention Concept Erasure in Text-to-Image Diffusion Models. CoRR Yuyang Xue, Edward Moroshko, Feng Chen, Steven McDonagh, Sotirios A. Tsaftaris
2025	J	`jnl`	Do generative models learn rare generative factors? Frontiers Artif. Intell. Fasih Haider, Edward Moroshko, Yuyang Xue, Sotirios A. Tsaftaris
2023	A*	`conf`	Continual Learning in Linear Classification on Separable Data. ICML Itay Evron, Edward Moroshko, Gon Buzaglo, Maroun Khriesh, Badea Marjieh, Nathan Srebro, Daniel Soudry
2023	J	`jnl`	Continual Learning in Linear Classification on Separable Data. CoRR Itay Evron, Edward Moroshko, Gon Buzaglo, Maroun Khriesh, Badea Marjieh, Nathan Srebro, Daniel Soudry
2022	A*	`conf`	Finite Sample Analysis Of Dynamic Regression Parameter Learning. NeurIPS Mark Kozdoba, Edward Moroshko, Shie Mannor, Yacov Crammer
2022	A*	`conf`	How catastrophic can catastrophic forgetting be in linear regression? COLT Itay Evron, Edward Moroshko, Rachel A. Ward, Nathan Srebro, Daniel Soudry
2022	J	`jnl`	How catastrophic can catastrophic forgetting be in linear regression? CoRR Itay Evron, Edward Moroshko, Rachel A. Ward, Nati Srebro, Daniel Soudry
2021	A*	`conf`	On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent. ICML Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E. Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry
2021	J	`jnl`	On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent. CoRR Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E. Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry
2020	A*	`conf`	Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy. NeurIPS Edward Moroshko, Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Nati Srebro, Daniel Soudry
2020	J	`jnl`	Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy. CoRR Edward Moroshko, Suriya Gunasekar, Blake E. Woodworth, Jason D. Lee, Nathan Srebro, Daniel Soudry
2020	A*	`conf`	Kernel and Rich Regimes in Overparametrized Models. COLT Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, Nathan Srebro
2020	J	`jnl`	Kernel and Rich Regimes in Overparametrized Models. CoRR Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, Nathan Srebro
2019	J	`jnl`	An Editorial Network for Enhanced Document Summarization. CoRR Edward Moroshko, Guy Feigenblat, Haggai Roitman, David Konopnicki
2019	J	`jnl`	Variance Estimation For Online Regression via Spectrum Thresholding. CoRR Mark Kozdoba, Edward Moroshko, Shie Mannor, Koby Crammer
2018	J	`jnl`	Efficient Loss-Based Decoding On Graphs For Extreme Classification. CoRR Itay Evron, Edward Moroshko, Koby Crammer
2018	A*	`conf`	Efficient Loss-Based Decoding on Graphs for Extreme Classification. NeurIPS Itay Evron, Edward Moroshko, Koby Crammer
2018	J	`jnl`	Multi Instance Learning For Unbalanced Data. CoRR Mark Kozdoba, Edward Moroshko, Lior Shani, Takuya Takagi, Takashi Katoh, Shie Mannor, Koby Crammer
2017	—	`conf`	Online Regression with Controlled Label Noise Rate. ECML/PKDD (2) Edward Moroshko, Koby Crammer
2015	J	`jnl`	Second-order non-stationary online learning for regression. J. Mach. Learn. Res. Edward Moroshko, Nina Vaits, Koby Crammer
2014	A	`conf`	Selective Sampling with Drift. AISTATS Edward Moroshko, Koby Crammer
2014	J	`jnl`	Selective Sampling with Drift. CoRR Edward Moroshko, Koby Crammer
2014	J	`jnl`	Weighted last-step min-max algorithm with improved sub-logarithmic regret. Theor. Comput. Sci. Edward Moroshko, Koby Crammer
2013	J	`jnl`	A Last-Step Regression Algorithm for Non-Stationary Online Learning CoRR Edward Moroshko, Koby Crammer
2013	A	`conf`	A Last-Step Regression Algorithm for Non-Stationary Online Learning. AISTATS Edward Moroshko, Koby Crammer
2013	J	`jnl`	Second-Order Non-Stationary Online Learning for Regression CoRR Nina Vaits, Edward Moroshko, Koby Crammer
2013	J	`jnl`	Weighted Last-Step Min-Max Algorithm with Improved Sub-Logarithmic Regret CoRR Edward Moroshko, Koby Crammer
2012	B	`conf`	Weighted Last-Step Min-Max Algorithm with Improved Sub-logarithmic Regret. ALT Edward Moroshko, Koby Crammer

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.