Takashi Takenouchi

48 papers A* 2A 4B 3C 2Journal 27Unranked 10

Year	Rank	Type	Title / Venue / Authors
2025	—	`conf`	Bayesian-Like Estimation with Unnormalized Model. GSI (1) Takashi Takenouchi
2022	J	`jnl`	Improving imbalanced classification using near-miss instances. Expert Syst. Appl. Akira Tanimoto, So Yamada, Takashi Takenouchi, Masashi Sugiyama, Hisashi Kashima
2022	J	`jnl`	Representation Learning for Maximization of MI, Nonlinear ICA and Nonlinear Subspaces with Robust Density Ratio Estimation. J. Mach. Learn. Res. Hiroaki Sasaki, Takashi Takenouchi
2021	J	`jnl`	A unified view for unsupervised representation learning with density ratio estimation: Maximization of mutual information, nonlinear ICA and nonlinear subspace estimation. CoRR Hiroaki Sasaki, Takashi Takenouchi
2021	—	`conf`	Causal Combinatorial Factorization Machines for Set-Wise Recommendation. PAKDD (2) Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima
2021	A*	`conf`	Lower-Bounded Proper Losses for Weakly Supervised Classification. ICML Shuhei M. Yoshida, Takashi Takenouchi, Masashi Sugiyama
2021	J	`jnl`	Lower-bounded proper losses for weakly supervised classification. CoRR Shuhei M. Yoshida, Takashi Takenouchi, Masashi Sugiyama
2021	A	`conf`	Regret Minimization for Causal Inference on Large Treatment Space. AISTATS Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima
2020	A	`conf`	A Unified Statistically Efficient Estimation Framework for Unnormalized Models. AISTATS Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi, Takeru Matsuda
2020	A	`conf`	Partially Zero-shot Domain Adaptation from Incomplete Target Data with Missing Classes. WACV Masato Ishii, Takashi Takenouchi, Masashi Sugiyama
2020	J	`jnl`	Regret Minimization for Causal Inference on Large Treatment Space. CoRR Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima
2020	A	`conf`	Robust contrastive learning and nonlinear ICA in the presence of outliers. UAI Hiroaki Sasaki, Takashi Takenouchi, Ricardo Pio Monti, Aapo Hyvärinen
2019	—	`conf`	Parameter Estimation with Generalized Empirical Localization. GSI Takashi Takenouchi
2019	J	`jnl`	Robust contrastive learning and nonlinear ICA in the presence of outliers. CoRR Hiroaki Sasaki, Takashi Takenouchi, Ricardo Pio Monti, Aapo Hyvärinen
2019	J	`jnl`	Unified estimation framework for unnormalized models with statistical efficiency. CoRR Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi, Takeru Matsuda
2019	C	`conf`	Zero-shot Domain Adaptation Based on Attribute Information. ACML Masato Ishii, Takashi Takenouchi, Masashi Sugiyama
2019	J	`jnl`	Zero-shot Domain Adaptation Based on Attribute Information. CoRR Masato Ishii, Takashi Takenouchi, Masashi Sugiyama
2018	J	`jnl`	Binary classifiers ensemble based on Bregman divergence for multi-class classification. Neurocomputing Takashi Takenouchi, Shin Ishii
2017	J	`jnl`	Graph-based composite local Bregman divergences on discrete sample spaces. Neural Networks Takafumi Kanamori, Takashi Takenouchi
2017	J	`jnl`	Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences. J. Mach. Learn. Res. Takashi Takenouchi, Takafumi Kanamori
2015	J	`jnl`	A Novel Parameter Estimation Method for Boltzmann Machines. Neural Comput. Takashi Takenouchi
2015	J	`jnl`	Binary Classification with a Pseudo Exponential Model and Its Application for Multi-Task Learning. Entropy Takashi Takenouchi, Osamu Komori, Shinto Eguchi
2015	—	`conf`	Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces. NIPS Takashi Takenouchi, Takafumi Kanamori
2015	B	`conf`	Non-negative Matrix Factorization based on γ-divergence. IJCNN Kohei Machida, Takashi Takenouchi
2013	J	`jnl`	Improving Logitboost with prior knowledge. Inf. Fusion Takafumi Kanamori, Takashi Takenouchi
2012	—	`conf`	A Unified Framework of Binary Classifiers Ensemble for Multi-class Classification. ICONIP (2) Takashi Takenouchi, Shin Ishii
2012	J	`jnl`	An Extension of the Receiver Operating Characteristic Curve and AUC-Optimal Classification. Neural Comput. Takashi Takenouchi, Osamu Komori, Shinto Eguchi
2012	—	`conf`	Self-measuring Similarity for Multi-task Gaussian Process. ICML Unsupervised and Transfer Learning Kohei Hayashi, Takashi Takenouchi, Ryota Tomioka, Hisashi Kashima
2012	J	`jnl`	Subsurface imaging for anti-personal mine detection by Bayesian super-resolution with a smooth-gap prior. Artif. Life Robotics Satoshi Kozawa, Takashi Takenouchi, Kazushi Ikeda
2011	J	`jnl`	Exponential family tensor factorization: an online extension and applications. Knowl. Inf. Syst. Kohei Hayashi, Takashi Takenouchi, Tomohiro Shibata, Yuki Kamiya, Daishi Kato, Kazuo Kunieda, Keiji Yamada, Kazushi Ikeda
2011	J	`jnl`	Ternary Bradley-Terry model-based decoding for multi-class classification and its extensions. Mach. Learn. Takashi Takenouchi, Shin Ishii
2010	A*	`conf`	Exponential Family Tensor Factorization for Missing-Values Prediction and Anomaly Detection. ICDM Kohei Hayashi, Takashi Takenouchi, Tomohiro Shibata, Yuki Kamiya, Daishi Kato, Kazuo Kunieda, Keiji Yamada, Kazushi Ikeda
2010	—	`conf`	Theoretical Analysis of Cross-Validation(CV)-EM Algorithm. ICANN (3) Takashi Takenouchi, Kazushi Ikeda
2009	J	`jnl`	A Multiclass Classification Method Based on Decoding of Binary Classifiers. Neural Comput. Takashi Takenouchi, Shin Ishii
2008	J	`jnl`	A probabilistic modeling of MOSAIC learning. Artif. Life Robotics Satoshi Osaga, Junichiro Hirayama, Takashi Takenouchi, Shin Ishii
2008	J	`jnl`	Robust Boosting Algorithm Against Mislabeling in Multiclass Problems. Neural Comput. Takashi Takenouchi, Shinto Eguchi, Noboru Murata, Takafumi Kanamori
2007	—	`conf`	A Probabilistic Model of MOSAIC. FOCI Satoshi Osaga, Junichiro Hirayama, Takashi Takenouchi, Shin Ishii
2007	B	`conf`	A probabilistic decoding approach to multi-class classification. IJCNN Takashi Takenouchi, Shin Ishii
2007	—	`conf`	Bayesian Collaborative Predictors for General User Modeling Tasks. ICONIP (1) Junichiro Hirayama, Masashi Nakatomi, Takashi Takenouchi, Shin Ishii
2007	J	`jnl`	GroupAdaBoost: Accurate Prediction and Selection of Important Genes. Inf. Media Technol. Takashi Takenouchi, Masaru Ushijima, Shinto Eguchi
2007	—	`conf`	Multiclass classification as a decoding problem. FOCI Takashi Takenouchi, Shin Ishii
2007	J	`jnl`	Parameter estimation for von Mises-Fisher distributions. Comput. Stat. Akihiro Tanabe, Kenji Fukumizu, Shigeyuki Oba, Takashi Takenouchi, Shin Ishii
2007	J	`jnl`	Robust Loss Functions for Boosting. Neural Comput. Takafumi Kanamori, Takashi Takenouchi, Shinto Eguchi, Noboru Murata
2006	J	`jnl`	Geometrical Structure of Boosting Algorithm. New Gener. Comput. Takafumi Kanamori, Takashi Takenouchi, Noboru Murata
2005	C	`conf`	GroupAdaBoost for Selecting Important Genes. BIBE Takashi Takenouchi, Masaru Ushijima, Shinto Eguchi
2004	J	`jnl`	Information Geometry of U-Boost and Bregman Divergence. Neural Comput. Noboru Murata, Takashi Takenouchi, Takafumi Kanamori, Shinto Eguchi
2004	J	`jnl`	Robustifying AdaBoost by Adding the Naive Error Rate. Neural Comput. Takashi Takenouchi, Shinto Eguchi
2004	B	`conf`	The Most Robust Loss Function for Boosting. ICONIP Takafumi Kanamori, Takashi Takenouchi, Shinto Eguchi, Noboru Murata

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.