Taisuke Yasuda

38 papers A* 16A 1Journal 19Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	John Ellipsoids via Lazy Updates. CoRR David P. Woodruff, Taisuke Yasuda
2025	J	`jnl`	Online Lewis Weight Sampling. ACM Trans. Algorithms David P. Woodruff, Taisuke Yasuda
2025	A*	`conf`	Root Ridge Leverage Score Sampling for ℓp Subspace Approximation. FOCS David P. Woodruff, Taisuke Yasuda
2025	A*	`conf`	Streaming Algorithms For ℓp Flows and ℓp Regression. ICLR Amit Chakrabarti, Jeffrey Jiang, David P. Woodruff, Taisuke Yasuda
2024	—	`—`	Algorithms for Matrix Approximation: Sketching, Sampling, and Sparse Optimization Taisuke Yasuda
2024	J	`jnl`	Coresets for Multiple ℓ CoRR David P. Woodruff, Taisuke Yasuda
2024	A*	`conf`	Coresets for Multiple ℓp Regression. ICML David P. Woodruff, Taisuke Yasuda
2024	A*	`conf`	John Ellipsoids via Lazy Updates. NeurIPS David P. Woodruff, Taisuke Yasuda
2024	J	`jnl`	Nearly Linear Sparsification of ℓ CoRR David P. Woodruff, Taisuke Yasuda
2024	A*	`conf`	Reweighted Solutions for Weighted Low Rank Approximation. ICML David P. Woodruff, Taisuke Yasuda
2024	J	`jnl`	Reweighted Solutions for Weighted Low Rank Approximation. CoRR David P. Woodruff, Taisuke Yasuda
2024	A*	`conf`	SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization. NeurIPS Taisuke Yasuda, Kyriakos Axiotis, Gang Fu, Mohammad Hossein Bateni, Vahab Mirrokni
2024	J	`jnl`	SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization. CoRR Taisuke Yasuda, Kyriakos Axiotis, Gang Fu, MohammadHossein Bateni, Vahab Mirrokni
2023	A*	`conf`	New Subset Selection Algorithms for Low Rank Approximation: Offline and Online. STOC David P. Woodruff, Taisuke Yasuda
2023	J	`jnl`	New Subset Selection Algorithms for Low Rank Approximation: Offline and Online. CoRR David P. Woodruff, Taisuke Yasuda
2023	A*	`conf`	Online Lewis Weight Sampling. SODA David P. Woodruff, Taisuke Yasuda
2023	J	`jnl`	Performance of 𝓁 CoRR Kyriakos Axiotis, Taisuke Yasuda
2023	A*	`conf`	Sequential Attention for Feature Selection. ICLR Taisuke Yasuda, Mohammad Hossein Bateni, Lin Chen, Matthew Fahrbach, Gang Fu, Vahab Mirrokni
2023	A*	`conf`	Sharper Bounds for ℓ ICML David P. Woodruff, Taisuke Yasuda
2023	J	`jnl`	Sharper Bounds for 𝓁 CoRR David P. Woodruff, Taisuke Yasuda
2023	A*	`conf`	Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming. NeurIPS Gregory Dexter, Petros Drineas, David P. Woodruff, Taisuke Yasuda
2023	J	`jnl`	Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming. CoRR Gregory Dexter, Petros Drineas, David P. Woodruff, Taisuke Yasuda
2022	A*	`conf`	Active Linear Regression for ℓp Norms and Beyond. FOCS Cameron Musco, Christopher Musco, David P. Woodruff, Taisuke Yasuda
2022	A*	`conf`	High-Dimensional Geometric Streaming in Polynomial Space. FOCS David P. Woodruff, Taisuke Yasuda
2022	J	`jnl`	High-Dimensional Geometric Streaming in Polynomial Space. CoRR David P. Woodruff, Taisuke Yasuda
2022	A*	`conf`	Improved Algorithms for Low Rank Approximation from Sparsity. SODA David P. Woodruff, Taisuke Yasuda
2022	J	`jnl`	Online Lewis Weight Sampling. CoRR David P. Woodruff, Taisuke Yasuda
2022	J	`jnl`	Sequential Attention for Feature Selection. CoRR MohammadHossein Bateni, Lin Chen, Matthew Fahrbach, Gang Fu, Vahab Mirrokni, Taisuke Yasuda
2021	J	`jnl`	Active Sampling for Linear Regression Beyond the $\ell_2$ Norm. CoRR Cameron Musco, Christopher Musco, David P. Woodruff, Taisuke Yasuda
2021	A*	`conf`	Exponentially Improved Dimensionality Reduction for l1: Subspace Embeddings and Independence Testing. COLT Yi Li, David P. Woodruff, Taisuke Yasuda
2021	J	`jnl`	Exponentially Improved Dimensionality Reduction for 𝓁 CoRR Yi Li, David P. Woodruff, Taisuke Yasuda
2021	J	`jnl`	Improved Algorithms for Low Rank Approximation from Sparsity. CoRR David P. Woodruff, Taisuke Yasuda
2020	A	`conf`	Graph Spanners in the Message-Passing Model. ITCS Manuel Fernandez, David P. Woodruff, Taisuke Yasuda
2019	J	`jnl`	Graph Spanners in the Message-Passing Model. CoRR Manuel Fernandez, David P. Woodruff, Taisuke Yasuda
2019	—	`conf`	The Query Complexity of Mastermind with l APPROX-RANDOM Manuel Fernandez, David P. Woodruff, Taisuke Yasuda
2019	J	`jnl`	The Query Complexity of Mastermind with 𝓁 CoRR Manuel Fernandez, David P. Woodruff, Taisuke Yasuda
2019	A*	`conf`	Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel $k$-means Clustering. ICML Taisuke Yasuda, David P. Woodruff, Manuel Fernandez
2019	J	`jnl`	Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel k-means Clustering. CoRR Manuel Fernandez, David P. Woodruff, Taisuke Yasuda

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.