A New Sequential Algorithm for Regression Problems by Using Mixture Distribution. - Takafumi Kanamori (ICANN, 2002)

Robust Regression with Asymmetric Heavy-Tail Noise Distributions. - Ichiro Takeuchi; Yoshua Bengio; Takafumi Kanamori (Neural Comput., 2002)

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)

The Entire Solution Path of Kernel-based Nonparametric Conditional Quantile Estimator. - Ichiro Takeuchi; Kaname Nomura; Takafumi Kanamori (IJCNN, 2006)

Conditional mean estimation under asymmetric and heteroscedastic error by linear combination of quantile regressions. - Takafumi Kanamori; Ichiro Takeuchi (Comput. Stat. Data Anal., 2006)

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

Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability. - Takafumi Kanamori (IEICE Trans. Inf. Syst., 2007)

Pool-based active learning with optimal sampling distribution and its information geometrical interpretation. - Takafumi Kanamori (Neurocomputing, 2007)

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

Inlier-Based Outlier Detection via Direct Density Ratio Estimation. - Shohei Hido; Yuta Tsuboi; Hisashi Kashima; Masashi Sugiyama; Takafumi Kanamori (ICDM, 2008)

Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection. - Takafumi Kanamori; Shohei Hido; Masashi Sugiyama (NIPS, 2008)

Approximating Mutual Information by Maximum Likelihood Density Ratio Estimation. - Taiji Suzuki; Masashi Sugiyama; Jun Sese; Takafumi Kanamori (FSDM, 2008)

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

Mutual information estimation reveals global associations between stimuli and biological processes. - Taiji Suzuki; Masashi Sugiyama; Takafumi Kanamori; Jun Sese (BMC Bioinform., 2009)

A robust approach based on conditional value-at-risk measure to statistical learning problems. - Akiko Takeda; Takafumi Kanamori (Eur. J. Oper. Res., 2009)

A Density-ratio Framework for Statistical Data Processing. - Masashi Sugiyama; Takafumi Kanamori; Taiji Suzuki; Shohei Hido; Jun Sese; Ichiro Takeuchi; Liwei Wang 001 (IPSJ Trans. Comput. Vis. Appl., 2009)

A Least-squares Approach to Direct Importance Estimation. - Takafumi Kanamori; Shohei Hido; Masashi Sugiyama (J. Mach. Learn. Res., 2009)

Nonparametric Conditional Density Estimation Using Piecewise-Linear Solution Path of Kernel Quantile Regression. - Ichiro Takeuchi; Kaname Nomura; Takafumi Kanamori (Neural Comput., 2009)

Direct Density Ratio Estimation with Dimensionality Reduction. - Masashi Sugiyama; Satoshi Hara 001; Paul von Bünau; Taiji Suzuki; Takafumi Kanamori; Motoaki Kawanabe (SDM, 2010)

Theoretical Analysis of Density Ratio Estimation. - Takafumi Kanamori; Taiji Suzuki; Masashi Sugiyama (IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2010)

Least-Squares Conditional Density Estimation. - Masashi Sugiyama; Ichiro Takeuchi; Taiji Suzuki; Takafumi Kanamori; Hirotaka Hachiya; Daisuke Okanohara (IEICE Trans. Inf. Syst., 2010)

Conditional Density Estimation via Least-Squares Density Ratio Estimation. - Masashi Sugiyama; Ichiro Takeuchi; Taiji Suzuki; Takafumi Kanamori; Hirotaka Hachiya; Daisuke Okanohara (AISTATS, 2010)

Deformation of log-likelihood loss function for multiclass boosting. - Takafumi Kanamori (Neural Networks, 2010)

Multiscale Bagging and Its Applications. - Hidetoshi Shimodaira; Takafumi Kanamori; Masayoshi Aoki; Kouta Mine (IEICE Trans. Inf. Syst., 2011)

Statistical outlier detection using direct density ratio estimation. - Shohei Hido; Yuta Tsuboi; Hisashi Kashima; Masashi Sugiyama; Takafumi Kanamori (Knowl. Inf. Syst., 2011)

Least-squares two-sample test. - Masashi Sugiyama; Taiji Suzuki; Yuta Itoh 001; Takafumi Kanamori; Manabu Kimura (Neural Networks, 2011)

Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search. - Masashi Sugiyama; Makoto Yamada; Paul von Bünau; Taiji Suzuki; Takafumi Kanamori; Motoaki Kawanabe (Neural Networks, 2011)

Density Ratio Estimation in Machine Learning. - Masashi Sugiyama; Taiji Suzuki; Takafumi Kanamori (Cambridge University Press, 2012)

A Unified Robust Classification Model. - Akiko Takeda; Hiroyuki Mitsugi; Takafumi Kanamori (ICML, 2012)

Non-convex Optimization on Stiefel Manifold and Applications to Machine Learning. - Takafumi Kanamori; Akiko Takeda (ICONIP (1), 2012)

A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems. - Takafumi Kanamori; Akiko Takeda; Taiji Suzuki (COLT, 2012)

Worst-Case Violation of Sampled Convex Programs for Optimization with Uncertainty. - Takafumi Kanamori; Akiko Takeda (J. Optim. Theory Appl., 2012)

Statistical analysis of kernel-based least-squares density-ratio estimation. - Takafumi Kanamori; Taiji Suzuki; Masashi Sugiyama (Mach. Learn., 2012)

f-Divergence Estimation and Two-Sample Homogeneity Test Under Semiparametric Density-Ratio Models. - Takafumi Kanamori; Taiji Suzuki; Masashi Sugiyama (IEEE Trans. Inf. Theory, 2012)

Statistical models and learning algorithms for ordinal regression problems. - Takafumi Kanamori (Inf. Fusion, 2013)

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

A Bregman extension of quasi-Newton updates II: Analysis of robustness properties. - Takafumi Kanamori; Atsumi Ohara (J. Comput. Appl. Math., 2013)

Direct Divergence Approximation between Probability Distributions and Its Applications in Machine Learning. - Masashi Sugiyama; Song Liu 002; Marthinus Christoffel du Plessis; Masao Yamanaka; Makoto Yamada; Taiji Suzuki; Takafumi Kanamori (J. Comput. Sci. Eng., 2013)

Conjugate relation between loss functions and uncertainty sets in classification problems. - Takafumi Kanamori; Akiko Takeda; Taiji Suzuki (J. Mach. Learn. Res., 2013)

Computational complexity of kernel-based density-ratio estimation: a condition number analysis. - Takafumi Kanamori; Taiji Suzuki; Masashi Sugiyama (Mach. Learn., 2013)

Semi-supervised learning with density-ratio estimation. - Masanori Kawakita; Takafumi Kanamori (Mach. Learn., 2013)

Density-Difference Estimation. - Masashi Sugiyama; Takafumi Kanamori; Taiji Suzuki; Marthinus Christoffel du Plessis; Song Liu 002; Ichiro Takeuchi (Neural Comput., 2013)

A Unified Classification Model Based on Robust Optimization. - Akiko Takeda; Hiroyuki Mitsugi; Takafumi Kanamori (Neural Comput., 2013)

Relative Density-Ratio Estimation for Robust Distribution Comparison. - Makoto Yamada; Taiji Suzuki; Takafumi Kanamori; Hirotaka Hachiya; Masashi Sugiyama (Neural Comput., 2013)

A Bregman extension of quasi-Newton updates I: an information geometrical framework. - Takafumi Kanamori; Atsumi Ohara (Optim. Methods Softw., 2013)

Numerical study of learning algorithms on Stiefel manifold. - Takafumi Kanamori; Akiko Takeda (Comput. Manag. Sci., 2014)

Breakdown Point of Robust Support Vector Machine. - Takafumi Kanamori; Shuhei Fujiwara; Akiko Takeda (CoRR, 2014)

Scale-Invariant Divergences for Density Functions. - Takafumi Kanamori (Entropy, 2014)

Statistical Analysis of Distance Estimators with Density Differences and Density Ratios. - Takafumi Kanamori; Masashi Sugiyama (Entropy, 2014)

Constrained Least-Squares Density-Difference Estimation. - Tuan Duong Nguyen; Marthinus Christoffel du Plessis; Takafumi Kanamori; Masashi Sugiyama (IEICE Trans. Inf. Syst., 2014)

Extended Robust Support Vector Machine Based on Financial Risk Minimization. - Akiko Takeda; Shuhei Fujiwara; Takafumi Kanamori (Neural Comput., 2014)

Using financial risk measures for analyzing generalization performance of machine learning models. - Akiko Takeda; Takafumi Kanamori (Neural Networks, 2014)

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

Efficiency Bound of Local Z-Estimators on Discrete Sample Spaces. - Takafumi Kanamori (Entropy, 2016)

Estimating Density Ridges by Direct Estimation of Density-Derivative-Ratios. - Hiroaki Sasaki; Takafumi Kanamori; Masashi Sugiyama (AISTATS, 2017)

Breakdown Point of Robust Support Vector Machines. - Takafumi Kanamori; Shuhei Fujiwara; Akiko Takeda (Entropy, 2017)

Parallel distributed block coordinate descent methods based on pairwise comparison oracle. - Kota Matsui; Wataru Kumagai; Takafumi Kanamori (J. Glob. Optim., 2017)

Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios. - Hiroaki Sasaki; Takafumi Kanamori; Aapo Hyvärinen; Gang Niu 001; Masashi Sugiyama (J. Mach. Learn. Res., 2017)

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

DC Algorithm for Extended Robust Support Vector Machine. - Shuhei Fujiwara; Akiko Takeda; Takafumi Kanamori (Neural Comput., 2017)

Robustness of learning algorithms using hinge loss with outlier indicators. - Takafumi Kanamori; Shuhei Fujiwara; Akiko Takeda (Neural Networks, 2017)

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

Fisher Efficient Inference of Intractable Models. - Song Liu 002; Takafumi Kanamori; Wittawat Jitkrittum; Yu Chen (NeurIPS, 2019)

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

Estimating Density Models with Complex Truncation Boundaries. - Song Liu 002; Takafumi Kanamori (CoRR, 2019)

Robust modal regression with direct log-density derivative estimation. - Hiroaki Sasaki; Tomoya Sakai 001; Takafumi Kanamori (CoRR, 2019)

Model Description of Similarity-Based Recommendation Systems. - Takafumi Kanamori; Naoya Osugi (Entropy, 2019)

Spectral Embedded Deep Clustering. - Yuichiro Wada; Shugo Miyamoto; Takumi Nakagama; Léo Andéol; Wataru Kumagai; Takafumi Kanamori (Entropy, 2019)

Robust Label Prediction via Label Propagation and Geodesic k-Nearest Neighbor in Online Semi-Supervised Learning. - Yuichiro Wada; Siqiang Su; Wataru Kumagai; Takafumi Kanamori (IEICE Trans. Inf. Syst., 2019)

Risk bound of transfer learning using parametric feature mapping and its application to sparse coding. - Wataru Kumagai; Takafumi Kanamori (Mach. Learn., 2019)

Variable Selection for Nonparametric Learning with Power Series Kernels. - Kota Matsui; Wataru Kumagai; Kenta Kanamori; Mitsuaki Nishikimi; Takafumi Kanamori (Neural Comput., 2019)

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

Robust modal regression with direct gradient approximation of modal regression risk. - Hiroaki Sasaki; Tomoya Sakai 001; Takafumi Kanamori (UAI, 2020)

Uncertainty propagation for dropout-based Bayesian neural networks. - Yuki Mae; Wataru Kumagai; Takafumi Kanamori (Neural Networks, 2021)

Mode estimation on matrix manifolds: Convergence and robustness. - Hiroaki Sasaki; Jun-Ichiro Hirayama; Takafumi Kanamori (AISTATS, 2022)

Deep Self-Supervised Learning of Speech Denoising from Noisy Speeches. - Yutaro Sanada; Takumi Nakagawa; Yuichiro Wada; Kosaku Takanashi; Yuhui Zhang; Kiichi Tokuyama; Takafumi Kanamori; Tomonori Yamada (INTERSPEECH, 2022)

Estimating Density Models with Truncation Boundaries using Score Matching. - Song Liu 002; Takafumi Kanamori; Daniel J. Williams (J. Mach. Learn. Res., 2022)

Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis. - Hiroki Waida; Yuichiro Wada; Léo Andéol; Takumi Nakagawa; Yuhui Zhang; Takafumi Kanamori (ECML/PKDD (4), 2023)

A Convex Framework for Confounding Robust Inference. - Kei Ishikawa; Niao He; Takafumi Kanamori (CoRR, 2023)

Deep Clustering With a Constraint for Topological Invariance Based on Symmetric InfoNCE. - Yuhui Zhang; Yuichiro Wada; Hiroki Waida; Kaito Goto; Yusaku Hino; Takafumi Kanamori (Neural Comput., 2023)

Learning domain invariant representations by joint Wasserstein distance minimization. - Léo Andéol; Yusei Kawakami; Yuichiro Wada; Takafumi Kanamori; Klaus-Robert Müller; Grégoire Montavon (Neural Networks, 2023)

Robust VAEs via Generating Process of Noise Augmented Data. - Hiroo Irobe; Wataru Aoki; Kimihiro Yamazaki; Yuhui Zhang; Takumi Nakagawa; Hiroki Waida; Yuichiro Wada; Takafumi Kanamori (ISIT, 2024)

Robust Estimation for Kernel Exponential Families with Smoothed Total Variation Distances. - Takafumi Kanamori; Kodai Yokoyama; Takayuki Kawashima (CoRR, 2024)

Scaling-based Data Augmentation for Generative Models and its Theoretical Extension. - Yoshitaka Koike; Takumi Nakagawa; Hiroki Waida; Takafumi Kanamori (CoRR, 2024)

Denoising cosine similarity: A theory-driven approach for efficient representation learning. - Takumi Nakagawa; Yutaro Sanada; Hiroki Waida; Yuhui Zhang; Yuichiro Wada; Kosaku Takanashi; Tomonori Yamada; Takafumi Kanamori (Neural Networks, 2024)

NAS-DG: Ultra-Fast NAS via Uncertainty Modeling for Domain Generalization. - Keigo Wakayama; Takafumi Kanamori (IEEE Access, 2025)

Tensor dictionary-based heterogeneous transfer learning to study emotion-related gender differences in brain. - Lan Yang 010; Chen Qiao; Takafumi Kanamori; Vince D. Calhoun; Julia M. Stephen; Tony W. Wilson; Yu-Ping Wang 002 (Neural Networks, 2025)

TULiP: Test-time Uncertainty Estimation via Linearization and Weight Perturbation. - Yuhui Zhang; Dongshen Wu; Yuichiro Wada; Takafumi Kanamori (CoRR, 2025)