Uri Shaham - RankMe

44 papers A* 3A 2Journal 36Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	PRISM: PRIor from corpus Statistics for topic Modeling. CoRR Tal Ishon, Yoav Goldberg, Uri Shaham
2026	J	`jnl`	PRISM: PRIor from corpus Statistics for topic Modeling. Trans. Mach. Learn. Res. Tal Ishon, Yoav Goldberg, Uri Shaham
2026	J	`jnl`	RealStats: A Rigorous Real-Only Statistical Framework for Fake Image Detection. CoRR Haim Zisman, Uri Shaham
2026	J	`jnl`	Unsupervised Ensemble Learning Through Deep Energy-based Models. CoRR Ariel Maymon, Yanir Buznah, Uri Shaham
2025	J	`jnl`	Convexified Message-Passing Graph Neural Networks. CoRR Saar Cohen, Noa Agmon, Uri Shaham
2025	J	`jnl`	Enhancing VICReg: Random-Walk Pairing for Improved Generalization and Better Global Semantics Capturing. CoRR Idan Simai, Ronen Talmon, Uri Shaham
2025	J	`jnl`	Generalizable Spectral Embedding with an Application to UMAP. CoRR Nir Ben-Ari, Amitai Yacobi, Uri Shaham
2025	J	`jnl`	Generalizable Spectral Embedding with an Application to UMAP. Trans. Mach. Learn. Res. Nir Ben-Ari, Amitai Yacobi, Uri Shaham
2025	J	`jnl`	Generalizable and Robust Spectral Method for Multi-view Representation Learning. Trans. Mach. Learn. Res. Amitai Yacobi, Ofir Lindenbaum, Uri Shaham
2025	J	`jnl`	Learning Shared Representations from Unpaired Data. CoRR Amitai Yacobi, Nir Ben-Ari, Ronen Talmon, Uri Shaham
2025	J	`jnl`	P-CAFE: Personalized Cost-Aware Incremental Feature Selection For Electronic Health Records. CoRR Naama Kashani, Mira Cohen, Uri Shaham
2025	A	`conf`	P-CAFE: Personalized Cost-Aware Incremental Feature Selection for Electronic Health Records. ECAI Naama Kashani, Mira Cohen, Uri Shaham
2025	J	`jnl`	Provable Speech Attributes Conversion via Latent Independence. CoRR Jonathan Svirsky, Ofir Lindenbaum, Uri Shaham
2025	J	`jnl`	Turning Sand to Gold: Recycling Data to Bridge On-Policy and Off-Policy Learning via Causal Bound. CoRR Tal Fiskus, Uri Shaham
2024	J	`jnl`	G-SPARC: SPectral ARchitectures tackling the Cold-start problem in Graph learning. CoRR Yahel Jacobs, Reut Dayan, Uri Shaham
2024	A	`conf`	Sparse Binarization for Fast Keyword Spotting. INTERSPEECH Jonathan Svirsky, Uri Shaham, Ofir Lindenbaum
2024	J	`jnl`	Sparse Binarization for Fast Keyword Spotting. CoRR Jonathan Svirsky, Uri Shaham, Ofir Lindenbaum
2024	J	`jnl`	SpecRaGE: Robust and Generalizable Multi-view Spectral Representation Learning. CoRR Amitai Yacobi, Ofir Lindenbaum, Uri Shaham
2024	J	`jnl`	Supervised Domain Adaptation Based on Marginal and Conditional Distributions Alignment. Trans. Mach. Learn. Res. Ori Katz, Ronen Talmon, Uri Shaham
2022	J	`jnl`	Deep unsupervised feature selection by discarding nuisance and correlated features. Neural Networks Uri Shaham, Ofir Lindenbaum, Jonathan Svirsky, Yuval Kluger
2022	A*	`conf`	Discovery of Single Independent Latent Variable. NeurIPS Uri Shaham, Jonathan Svirsky, Ori Katz, Ronen Talmon
2021	J	`jnl`	Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features. CoRR Uri Shaham, Ofir Lindenbaum, Jonathan Svirsky, Yuval Kluger
2021	A*	`conf`	Differentiable Unsupervised Feature Selection based on a Gated Laplacian. NeurIPS Ofir Lindenbaum, Uri Shaham, Erez Peterfreund, Jonathan Svirsky, Nicolas Casey, Yuval Kluger
2021	J	`jnl`	Single Independent Component Recovery and Applications. CoRR Uri Shaham, Jonathan Svirsky, Ori Katz, Ronen Talmon
2020	J	`jnl`	Deep Ordinal Regression using Optimal Transport Loss and Unimodal Output Probabilities. CoRR Uri Shaham, Jonathan Svirsky
2020	—	`conf`	Learning to Ask Medical Questions using Reinforcement Learning. MLHC Uri Shaham, Tom Zahavy, Cesar Caraballo, Shiwani Mahajan, Daisy Massey, Harlan M. Krumholz
2020	J	`jnl`	Learning to Ask Medical Questions using Reinforcement Learning. CoRR Uri Shaham, Tom Zahavy, Cesar Caraballo, Shiwani Mahajan, Daisy Massey, Harlan M. Krumholz
2020	J	`jnl`	Let the Data Choose its Features: Differentiable Unsupervised Feature Selection. CoRR Ofir Lindenbaum, Uri Shaham, Jonathan Svirsky, Erez Peterfreund, Yuval Kluger
2018	J	`jnl`	Automated Characterization of Stenosis in Invasive Coronary Angiography Images with Convolutional Neural Networks. CoRR Benjamin Au, Uri Shaham, Sanket S. Dhruva, Georgios Bouras, Ecaterina Cristea, Alexandra Lansky, Andreas Coppi, Fred Warner, Shu-Xia Li, Harlan M. Krumholz
2018	J	`jnl`	Defending against Adversarial Images using Basis Functions Transformations. CoRR Uri Shaham, James Garritano, Yutaro Yamada, Ethan Weinberger, Alex Cloninger, Xiuyuan Cheng, Kelly P. Stanton, Yuval Kluger
2018	J	`jnl`	Learning by coincidence: Siamese networks and common variable learning. Pattern Recognit. Uri Shaham, Roy R. Lederman
2018	—	`conf`	SpectralNet: Spectral Clustering using Deep Neural Networks. ICLR (Poster) Uri Shaham, Kelly P. Stanton, Henry Li, Ronen Basri, Boaz Nadler, Yuval Kluger
2018	J	`jnl`	SpectralNet: Spectral Clustering using Deep Neural Networks. CoRR Uri Shaham, Kelly P. Stanton, Henry Li, Boaz Nadler, Ronen Basri, Yuval Kluger
2018	J	`jnl`	Understanding adversarial training: Increasing local stability of supervised models through robust optimization. Neurocomputing Uri Shaham, Yutaro Yamada, Sahand Negahban
2017	J	`jnl`	Gating mass cytometry data by deep learning. Bioinform. Huamin Li, Uri Shaham, Kelly P. Stanton, Yi Yao, Ruth R. Montgomery, Yuval Kluger
2017	J	`jnl`	Removal of batch effects using distribution-matching residual networks. Bioinform. Uri Shaham, Kelly P. Stanton, Jun Zhao, Huamin Li, Khadir Raddassi, Ruth R. Montgomery, Yuval Kluger
2016	A*	`conf`	A Deep Learning Approach to Unsupervised Ensemble Learning. ICML Uri Shaham, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph T. Chang, Yuval Kluger
2016	J	`jnl`	A Deep Learning Approach to Unsupervised Ensemble Learning. CoRR Uri Shaham, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph T. Chang, Yuval Kluger
2016	J	`jnl`	Deep Survival: A Deep Cox Proportional Hazards Network. CoRR Jared Katzman, Uri Shaham, Alexander Cloninger, Jonathan Bates, Tingting Jiang, Yuval Kluger
2015	J	`jnl`	Common Variable Learning and Invariant Representation Learning using Siamese Neural Networks. CoRR Uri Shaham, Roy R. Lederman
2015	J	`jnl`	Diffusion Nets. CoRR Gal Mishne, Uri Shaham, Alexander Cloninger, Israel Cohen
2015	J	`jnl`	Provable approximation properties for deep neural networks. CoRR Uri Shaham, Alexander Cloninger, Ronald R. Coifman
2015	J	`jnl`	Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization. CoRR Uri Shaham, Yutaro Yamada, Sahand Negahban
2008	—	`conf`	Classification using Normalized Compression Distance. Artificial Intelligence and Pattern Recognition Uri Shaham, Yael Edan

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.