Adam M. Johansen

27 papers A* 3A 1Journal 20Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Error bounds for particle gradient descent, and extensions of the log-Sobolev and Talagrand inequalities. J. Mach. Learn. Res. Rocco Caprio, Juan Kuntz, Samuel Power, Adam M. Johansen
2024	J	`jnl`	Error bounds for particle gradient descent, and extensions of the log-Sobolev and Talagrand inequalities. CoRR Rocco Caprio, Juan Kuntz, Samuel Power, Adam M. Johansen
2024	J	`jnl`	Fast convergence of the Expectation Maximization algorithm under a logarithmic Sobolev inequality. CoRR Rocco Caprio, Adam M. Johansen
2024	A*	`conf`	Momentum Particle Maximum Likelihood. ICML Jen Ning Lim, Juan Kuntz, Samuel Power, Adam M. Johansen
2024	A*	`conf`	Particle Semi-Implicit Variational Inference. NeurIPS Jen Ning Lim, Adam M. Johansen
2024	J	`jnl`	Particle Semi-Implicit Variational Inference. CoRR Jen Ning Lim, Adam M. Johansen
2024	J	`jnl`	Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty. CoRR Chatchuea Kimchaiwong, Jeremie Houssineau, Adam M. Johansen
2024	J	`jnl`	Solving Fredholm Integral Equations of the Second Kind via Wasserstein Gradient Flows. CoRR Francesca R. Crucinio, Adam M. Johansen
2023	J	`jnl`	Cost free hyper-parameter selection/averaging for Bayesian inverse problems with vanilla and Rao-Blackwellized SMC samplers. Stat. Comput. Alessandro Viani, Adam M. Johansen, Alberto Sorrentino
2023	J	`jnl`	Divide-and-Conquer Fusion. J. Mach. Learn. Res. Ryan S. Y. Chan, Murray Pollock, Adam M. Johansen, Gareth O. Roberts
2023	J	`jnl`	Momentum Particle Maximum Likelihood. CoRR Jen Ning Lim, Juan Kuntz, Samuel Power, Adam M. Johansen
2023	A	`conf`	Particle algorithms for maximum likelihood training of latent variable models. AISTATS Juan Kuntz, Jen Ning Lim, Adam M. Johansen
2022	J	`jnl`	Product-form estimators: exploiting independence to scale up Monte Carlo. Stat. Comput. Juan Kuntz, Francesca R. Crucinio, Adam M. Johansen
2022	J	`jnl`	Scalable particle-based alternatives to EM. CoRR Juan Kuntz, Adam M. Johansen
2022	J	`jnl`	Solving Fredholm Integral Equations of the First Kind via Wasserstein Gradient Flows. CoRR Francesca R. Crucinio, Valentin De Bortoli, Arnaud Doucet, Adam M. Johansen
2022	J	`jnl`	The node-wise Pseudo-marginal method: model selection with spatial dependence on latent graphs. Stat. Comput. Denishrouf Thesingarajah, Adam M. Johansen
2021	J	`jnl`	A spatio-temporal model to reveal oscillator phenotypes in molecular clocks: Parameter estimation elucidates circadian gene transcription dynamics in single-cells. PLoS Comput. Biol. Måns Unosson, Marco Brancaccio, Michael Hastings, Adam M. Johansen, Bärbel Finkenstädt
2021	J	`jnl`	Global Consensus Monte Carlo. J. Comput. Graph. Stat. Lewis J. Rendell, Adam M. Johansen, Anthony Lee, Nick Whiteley
2020	A*	`conf`	Generalised Bayesian Filtering via Sequential Monte Carlo. NeurIPS Ayman Boustati, Ömer Deniz Akyildiz, Theodoros Damoulas, Adam M. Johansen
2020	J	`jnl`	Generalized Bayesian Filtering via Sequential Monte Carlo. CoRR Ayman Boustati, Ömer Deniz Akyildiz, Theodoros Damoulas, Adam M. Johansen
2017	J	`jnl`	Bayesian model comparison with un-normalised likelihoods. Stat. Comput. Richard G. Everitt, Adam M. Johansen, Ellen Rowing, Melina Evdemon-Hogan
2015	J	`jnl`	Convergence of the k-Means Minimization Problem using Γ-Convergence. SIAM J. Appl. Math. Matthew Thorpe, Florian Theil, Adam M. Johansen, Neil Cade
2012	—	`conf`	Bayesian model comparison via path-sampling sequential Monte Carlo. SSP Yan Zhou, Adam M. Johansen, John A. D. Aston
2010	J	`jnl`	On solving integral equations using Markov chain Monte Carlo methods. Appl. Math. Comput. Arnaud Doucet, Adam M. Johansen, Vladislav Z. B. Tadic
2008	—	`ch.`	Markov Chains. Wiley Encyclopedia of Computer Science and Engineering Adam M. Johansen
2008	J	`jnl`	Particle methods for maximum likelihood estimation in latent variable models. Stat. Comput. Adam M. Johansen, Arnaud Doucet, Manuel Davy
2006	—	`conf`	Maximum Likelihood Parameter Estimation for Latent Variable Models Using Sequential Monte Carlo. ICASSP (3) Adam M. Johansen, Arnaud Doucet, Manuel Davy

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.