Fabian Schaipp

16 papers A* 2B 1Journal 12

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Step-Size Stability in Stochastic Optimization: A Theoretical Perspective. CoRR Fabian Schaipp, Robert M. Gower, Adrien B. Taylor
2025	J	`jnl`	Analysis of an Idealized Stochastic Polyak Method and its Application to Black-Box Model Distillation. CoRR Robert M. Gower, Guillaume Garrigos, Nicolas Loizou, Dimitris Oikonomou, Konstantin Mishchenko, Fabian Schaipp
2025	J	`jnl`	Optimization Benchmark for Diffusion Models on Dynamical Systems. CoRR Fabian Schaipp
2025	A*	`conf`	The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training. ICML Fabian Schaipp, Alexander Hägele, Adrien B. Taylor, Umut Simsekli, Francis Bach
2025	J	`jnl`	The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training. CoRR Fabian Schaipp, Alexander Hägele, Adrien B. Taylor, Umut Simsekli, Francis R. Bach
2025	J	`jnl`	Tracking the Median of Gradients with a Stochastic Proximal Point Method. Trans. Mach. Learn. Res. Fabian Schaipp, Guillaume Garrigos, Umut Simsekli, Robert M. Gower
2024	J	`jnl`	A Semismooth Newton Stochastic Proximal Point Algorithm with Variance Reduction. SIAM J. Optim. Andre Milzarek, Fabian Schaipp, Michael Ulbrich
2024	A*	`conf`	MoMo: Momentum Models for Adaptive Learning Rates. ICML Fabian Schaipp, Ruben Ohana, Michael Eickenberg, Aaron Defazio, Robert M. Gower
2024	J	`jnl`	SGD with Clipping is Secretly Estimating the Median Gradient. CoRR Fabian Schaipp, Guillaume Garrigos, Umut Simsekli, Robert M. Gower
2024	—	`—`	Topics in Stochastic Optimization: Learning with Implicit and Adaptive Steps (Implizite und Adaptive Methoden der Stochastischen Optimierung für Machine Learning) Fabian Schaipp
2023	J	`jnl`	A Stochastic Proximal Polyak Step Size. CoRR Fabian Schaipp, Robert M. Gower, Michael Ulbrich
2023	J	`jnl`	A Stochastic Proximal Polyak Step Size. Trans. Mach. Learn. Res. Fabian Schaipp, Robert M. Gower, Michael Ulbrich
2023	J	`jnl`	Function Value Learning: Adaptive Learning Rates Based on the Polyak Stepsize and Function Splitting in ERM. CoRR Guillaume Garrigos, Robert M. Gower, Fabian Schaipp
2023	J	`jnl`	MoMo: Momentum Models for Adaptive Learning Rates. CoRR Fabian Schaipp, Ruben Ohana, Michael Eickenberg, Aaron Defazio, Robert M. Gower
2021	J	`jnl`	GGLasso - a Python package for General Graphical Lasso computation. J. Open Source Softw. Fabian Schaipp, Oleg Vlasovets, Christian Müller
2020	B	`conf`	Simulation vs. Testbed: Small Scale Experimental Validation of an Open-Source LTE-A Model. PIMRC Lars Wischhof, Fabian Schaipp, Stefan Schuhbäck, Gerta Köster

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.