Benjamin Paul Chamberlain

44 papers A* 7A 1Journal 25Unranked 10

Year	Rank	Type	Title / Venue / Authors
2023	A*	`conf`	Gradient Gating for Deep Multi-Rate Learning on Graphs. ICLR T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra
2023	A*	`conf`	Graph Neural Networks for Link Prediction with Subgraph Sketching. ICLR Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Yannick Hammerla, Michael M. Bronstein, Max Hansmire
2023	A*	`conf`	Hyperbolic Deep Reinforcement Learning. ICLR Edoardo Cetin, Benjamin Paul Chamberlain, Michael M. Bronstein, Jonathan J. Hunt
2023	—	`conf`	The Second Learning on Graphs Conference: Preface. LoG Soledad Villar, Benjamin Paul Chamberlain, Yuanqi Du, Hannes Stärk, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Yanqiao Zhu, Kexin Huang, Michelle M. Li, Sofia Bourhim, Ilia Igashov, Alexandre Duval, Mathieu Alain, Dominique Beaini, Xinyu Yuan
2023	J	`jnl`	Understanding convolution on graphs via energies. Trans. Mach. Learn. Res. Francesco Di Giovanni, James Rowbottom, Benjamin Paul Chamberlain, Thomas Markovich, Michael M. Bronstein
2022	J	`jnl`	Gradient Gating for Deep Multi-Rate Learning on Graphs. CoRR T. Konstantin Rusch, Benjamin Paul Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra
2022	J	`jnl`	Graph Neural Networks as Gradient Flows. CoRR Francesco Di Giovanni, James Rowbottom, Benjamin Paul Chamberlain, Thomas Markovich, Michael M. Bronstein
2022	J	`jnl`	Graph Neural Networks for Link Prediction with Subgraph Sketching. CoRR Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Hammerla, Michael M. Bronstein, Max Hansmire
2022	J	`jnl`	Graph-Coupled Oscillator Networks. CoRR T. Konstantin Rusch, Benjamin Paul Chamberlain, James Rowbottom, Siddhartha Mishra, Michael M. Bronstein
2022	J	`jnl`	Hyperbolic Deep Reinforcement Learning. CoRR Edoardo Cetin, Benjamin Paul Chamberlain, Michael M. Bronstein, Jonathan J. Hunt
2022	A*	`conf`	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs. NeurIPS Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Lió, Michael M. Bronstein
2022	J	`jnl`	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs. CoRR Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Liò, Michael M. Bronstein
2022	—	`conf`	On the Unreasonable Effectiveness of Feature Propagation in Learning on Graphs With Missing Node Features. LoG Emanuele Rossi, Henry Kenlay, Maria I. Gorinova, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein
2022	A*	`conf`	Understanding over-squashing and bottlenecks on graphs via curvature. ICLR Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein
2021	J	`jnl`	Beltrami Flow and Neural Diffusion on Graphs. CoRR Benjamin Paul Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong, Michael M. Bronstein
2021	J	`jnl`	GRAND: Graph Neural Diffusion. CoRR Benjamin Paul Chamberlain, James Rowbottom, Maria I. Gorinova, Stefan Webb, Emanuele Rossi, Michael M. Bronstein
2021	J	`jnl`	On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features. CoRR Emanuele Rossi, Henry Kenlay, Maria I. Gorinova, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein
2021	J	`jnl`	Understanding over-squashing and bottlenecks on graphs via curvature. CoRR Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein
2020	—	`conf`	Fashion Outfit Generation for E-Commerce. ECML/PKDD (5) Elaine M. Bettaney, Stephen R. Hardwick, Odysseas Zisimopoulos, Benjamin Paul Chamberlain
2020	A	`conf`	Tuning Word2vec for Large Scale Recommendation Systems. RecSys Benjamin Paul Chamberlain, Emanuele Rossi, Dan Shiebler, Suvash Sedhain, Michael M. Bronstein
2020	J	`jnl`	Tuning Word2vec for Large Scale Recommendation Systems. CoRR Benjamin Paul Chamberlain, Emanuele Rossi, Dan Shiebler, Suvash Sedhain, Michael M. Bronstein
2020	J	`jnl`	What is the Value of Experimentation and Measurement? Data Sci. Eng. C. H. Bryan Liu, Benjamin Paul Chamberlain, Emma J. McCoy
2019	—	`conf`	Fashion Outfit Generation for E-commerce. eCOM@SIGIR Elaine M. Bettaney, Stephen R. Hardwick, Benjamin Paul Chamberlain, Odysseas Zisimopoulos
2019	J	`jnl`	Fashion Outfit Generation for E-commerce. CoRR Elaine M. Bettaney, Stephen R. Hardwick, Odysseas Zisimopoulos, Benjamin Paul Chamberlain
2019	—	`conf`	Learning Embeddings for Product Size Recommendations. eCOM@SIGIR Kallirroi Dogani, Matteo Tomassetti, Saúl Vargas, Benjamin Paul Chamberlain, Sofie De Cnudde
2019	J	`jnl`	Scalable Hyperbolic Recommender Systems. CoRR Benjamin Paul Chamberlain, Stephen R. Hardwick, David R. Wardrope, Fabon Dzogang, Fabio Daolio, Saúl Vargas
2019	A*	`conf`	What is the Value of Experimentation & Measurement? ICDM C. H. Bryan Liu, Benjamin Paul Chamberlain
2018	—	`conf`	A Recurrent Neural Network Survival Model: Predicting Web User Return Time. ECML/PKDD (3) Georg L. Grob, Ângelo Cardoso, C. H. Bryan Liu, Duncan A. Little, Benjamin Paul Chamberlain
2018	J	`jnl`	A Recurrent Neural Network Survival Model: Predicting Web User Return Time. CoRR Georg L. Grob, Ângelo Cardoso, C. H. Bryan Liu, Duncan A. Little, Benjamin Paul Chamberlain
2018	J	`jnl`	Designing Experiments to Measure Incrementality on Facebook. CoRR C. H. Bryan Liu, Elaine M. Bettaney, Benjamin Paul Chamberlain
2018	J	`jnl`	Online Controlled Experiments for Personalised e-Commerce Strategies: Design, Challenges, and Pitfalls. CoRR C. H. Bryan Liu, Benjamin Paul Chamberlain
2018	—	`—`	Practical challenges of learning and representation for large graphs. Benjamin Paul Chamberlain
2018	—	`conf`	Predicting Twitter User Socioeconomic Attributes with Network and Language Information. HT Nikolaos Aletras, Benjamin Paul Chamberlain
2018	J	`jnl`	Predicting Twitter User Socioeconomic Attributes with Network and Language Information. CoRR Nikolaos Aletras, Benjamin Paul Chamberlain
2018	—	`conf`	Speeding Up BigClam Implementation on SNAP. ICCSW C. H. Bryan Liu, Benjamin Paul Chamberlain
2017	J	`jnl`	Customer Life Time Value Prediction Using Embeddings. CoRR Benjamin Paul Chamberlain, Ângelo Cardoso, C. H. Bryan Liu, Roberto Pagliari, Marc Peter Deisenroth
2017	A*	`conf`	Customer Lifetime Value Prediction Using Embeddings. KDD Benjamin Paul Chamberlain, Ângelo Cardoso, C. H. Bryan Liu, Roberto Pagliari, Marc Peter Deisenroth
2017	—	`conf`	Generalising Random Forest Parameter Optimisation to Include Stability and Cost. ECML/PKDD (3) C. H. Bryan Liu, Benjamin Paul Chamberlain, Duncan A. Little, Ângelo Cardoso
2017	J	`jnl`	Generalising Random Forest Parameter Optimisation to Include Stability and Cost. CoRR C. H. Bryan Liu, Benjamin Paul Chamberlain, Duncan A. Little, Ângelo Cardoso
2017	J	`jnl`	Neural Embeddings of Graphs in Hyperbolic Space. CoRR Benjamin Paul Chamberlain, James R. Clough, Marc Peter Deisenroth
2017	—	`conf`	Probabilistic Inference of Twitter Users' Age Based on What They Follow. ECML/PKDD (3) Benjamin Paul Chamberlain, Clive Humby, Marc Peter Deisenroth
2017	J	`jnl`	Speeding Up BigClam Implementation on SNAP. CoRR C. H. Bryan Liu, Benjamin Paul Chamberlain
2016	J	`jnl`	Detecting the Age of Twitter Users. CoRR Benjamin Paul Chamberlain, Clive Humby, Marc Peter Deisenroth
2016	J	`jnl`	Real-Time Association Mining in Large Social Networks. CoRR Benjamin Paul Chamberlain, Josh Levy-Kramer, Clive Humby, Marc Peter Deisenroth

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.