The Most Robust Loss Function for Boosting. - Takafumi Kanamori; Takashi Takenouchi; Shinto Eguchi; Noboru Murata (ICONIP, 2004)

Information Geometry of U-Boost and Bregman Divergence. - Noboru Murata; Takashi Takenouchi; Takafumi Kanamori; Shinto Eguchi (Neural Comput., 2004)

Robustifying AdaBoost by Adding the Naive Error Rate. - Takashi Takenouchi; Shinto Eguchi (Neural Comput., 2004)

GroupAdaBoost for Selecting Important Genes. - Takashi Takenouchi; Masaru Ushijima; Shinto Eguchi (BIBE, 2005)

Geometrical Structure of Boosting Algorithm. - Takafumi Kanamori; Takashi Takenouchi; Noboru Murata (New Gener. Comput., 2006)

A Probabilistic Model of MOSAIC. - Satoshi Osaga; Junichiro Hirayama; Takashi Takenouchi; Shin Ishii (FOCI, 2007)

Multiclass classification as a decoding problem. - Takashi Takenouchi; Shin Ishii (FOCI, 2007)

Bayesian Collaborative Predictors for General User Modeling Tasks. - Junichiro Hirayama; Masashi Nakatomi; Takashi Takenouchi; Shin Ishii (ICONIP (1), 2007)

A probabilistic decoding approach to multi-class classification. - Takashi Takenouchi; Shin Ishii (IJCNN, 2007)

Parameter estimation for von Mises-Fisher distributions. - Akihiro Tanabe; Kenji Fukumizu; Shigeyuki Oba; Takashi Takenouchi; Shin Ishii (Comput. Stat., 2007)

GroupAdaBoost: Accurate Prediction and Selection of Important Genes. - Takashi Takenouchi; Masaru Ushijima; Shinto Eguchi (Inf. Media Technol., 2007)

Robust Loss Functions for Boosting. - Takafumi Kanamori; Takashi Takenouchi; Shinto Eguchi; Noboru Murata (Neural Comput., 2007)

A probabilistic modeling of MOSAIC learning. - Satoshi Osaga; Junichiro Hirayama; Takashi Takenouchi; Shin Ishii (Artif. Life Robotics, 2008)

Robust Boosting Algorithm Against Mislabeling in Multiclass Problems. - Takashi Takenouchi; Shinto Eguchi; Noboru Murata; Takafumi Kanamori (Neural Comput., 2008)

A Multiclass Classification Method Based on Decoding of Binary Classifiers. - Takashi Takenouchi; Shin Ishii (Neural Comput., 2009)

Theoretical Analysis of Cross-Validation(CV)-EM Algorithm. - Takashi Takenouchi; Kazushi Ikeda (ICANN (3), 2010)

Exponential Family Tensor Factorization for Missing-Values Prediction and Anomaly Detection. - Kohei Hayashi; Takashi Takenouchi; Tomohiro Shibata; Yuki Kamiya; Daishi Kato; Kazuo Kunieda; Keiji Yamada; Kazushi Ikeda (ICDM, 2010)

Exponential family tensor factorization: an online extension and applications. - Kohei Hayashi; Takashi Takenouchi; Tomohiro Shibata; Yuki Kamiya; Daishi Kato; Kazuo Kunieda; Keiji Yamada; Kazushi Ikeda (Knowl. Inf. Syst., 2011)

Ternary Bradley-Terry model-based decoding for multi-class classification and its extensions. - Takashi Takenouchi; Shin Ishii (Mach. Learn., 2011)

A Unified Framework of Binary Classifiers Ensemble for Multi-class Classification. - Takashi Takenouchi; Shin Ishii (ICONIP (2), 2012)

Subsurface imaging for anti-personal mine detection by Bayesian super-resolution with a smooth-gap prior. - Satoshi Kozawa; Takashi Takenouchi; Kazushi Ikeda (Artif. Life Robotics, 2012)

Self-measuring Similarity for Multi-task Gaussian Process. - Kohei Hayashi; Takashi Takenouchi; Ryota Tomioka; Hisashi Kashima (ICML Unsupervised and Transfer Learning, 2012)

An Extension of the Receiver Operating Characteristic Curve and AUC-Optimal Classification. - Takashi Takenouchi; Osamu Komori; Shinto Eguchi (Neural Comput., 2012)

Improving Logitboost with prior knowledge. - Takafumi Kanamori; Takashi Takenouchi (Inf. Fusion, 2013)

Non-negative Matrix Factorization based on γ-divergence. - Kohei Machida; Takashi Takenouchi (IJCNN, 2015)

Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces. - Takashi Takenouchi; Takafumi Kanamori (NIPS, 2015)

Binary Classification with a Pseudo Exponential Model and Its Application for Multi-Task Learning. - Takashi Takenouchi; Osamu Komori; Shinto Eguchi (Entropy, 2015)

A Novel Parameter Estimation Method for Boltzmann Machines. - Takashi Takenouchi (Neural Comput., 2015)

Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences. - Takashi Takenouchi; Takafumi Kanamori (J. Mach. Learn. Res., 2017)

Graph-based composite local Bregman divergences on discrete sample spaces. - Takafumi Kanamori; Takashi Takenouchi (Neural Networks, 2017)

Binary classifiers ensemble based on Bregman divergence for multi-class classification. - Takashi Takenouchi; Shin Ishii (Neurocomputing, 2018)

Zero-shot Domain Adaptation Based on Attribute Information. - Masato Ishii; Takashi Takenouchi; Masashi Sugiyama (ACML, 2019)

Parameter Estimation with Generalized Empirical Localization. - Takashi Takenouchi (GSI, 2019)

Unified estimation framework for unnormalized models with statistical efficiency. - Masatoshi Uehara; Takafumi Kanamori; Takashi Takenouchi; Takeru Matsuda (CoRR, 2019)

A Unified Statistically Efficient Estimation Framework for Unnormalized Models. - Masatoshi Uehara; Takafumi Kanamori; Takashi Takenouchi; Takeru Matsuda (AISTATS, 2020)

Robust contrastive learning and nonlinear ICA in the presence of outliers. - Hiroaki Sasaki; Takashi Takenouchi; Ricardo Pio Monti; Aapo Hyvärinen (UAI, 2020)

Partially Zero-shot Domain Adaptation from Incomplete Target Data with Missing Classes. - Masato Ishii; Takashi Takenouchi; Masashi Sugiyama (WACV, 2020)

Regret Minimization for Causal Inference on Large Treatment Space. - Akira Tanimoto; Tomoya Sakai 001; Takashi Takenouchi; Hisashi Kashima (AISTATS, 2021)

Lower-Bounded Proper Losses for Weakly Supervised Classification. - Shuhei M. Yoshida; Takashi Takenouchi; Masashi Sugiyama (ICML, 2021)

Causal Combinatorial Factorization Machines for Set-Wise Recommendation. - Akira Tanimoto; Tomoya Sakai 001; Takashi Takenouchi; Hisashi Kashima (PAKDD (2), 2021)

A unified view for unsupervised representation learning with density ratio estimation: Maximization of mutual information, nonlinear ICA and nonlinear subspace estimation. - Hiroaki Sasaki; Takashi Takenouchi (CoRR, 2021)

Improving imbalanced classification using near-miss instances. - Akira Tanimoto; So Yamada; Takashi Takenouchi; Masashi Sugiyama; Hisashi Kashima (Expert Syst. Appl., 2022)

Representation Learning for Maximization of MI, Nonlinear ICA and Nonlinear Subspaces with Robust Density Ratio Estimation. - Hiroaki Sasaki; Takashi Takenouchi (J. Mach. Learn. Res., 2022)