Takafumi Kanamori

According to our database, Takafumi Kanamori authored at least 83 papers between 2002 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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, January, 2024

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, October, 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., July, 2023

A Convex Framework for Confounding Robust Inference.

Kei Ishikawa, Niao He, Takafumi Kanamori

CoRR, 2023

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

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

2022

Estimating Density Models with Truncation Boundaries using Score Matching.

Song Liu, Takafumi Kanamori, Daniel J. Williams

J. Mach. Learn. Res., 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

Proceedings of the Interspeech 2022, 2022

Mode estimation on matrix manifolds: Convergence and robustness.

Hiroaki Sasaki, Jun-Ichiro Hirayama, Takafumi Kanamori

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Uncertainty propagation for dropout-based Bayesian neural networks.

Yuki Mae, Wataru Kumagai, Takafumi Kanamori

Neural Networks, 2021

2020

Robust modal regression with direct gradient approximation of modal regression risk.

Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

A Unified Statistically Efficient Estimation Framework for Unnormalized Models.

Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi, Takeru Matsuda

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Variable Selection for Nonparametric Learning with Power Series Kernels.

Kota Matsui, Wataru Kumagai, Kenta Kanamori, Mitsuaki Nishikimi, Takafumi Kanamori

Neural Comput., 2019

Risk bound of transfer learning using parametric feature mapping and its application to sparse coding.

Wataru Kumagai, Takafumi Kanamori

Mach. Learn., 2019

Robust Label Prediction via Label Propagation and Geodesic <i>k</i>-Nearest Neighbor in Online Semi-Supervised Learning.

Yuichiro Wada, Siqiang Su, Wataru Kumagai, Takafumi Kanamori

IEICE Trans. Inf. Syst., 2019

Spectral Embedded Deep Clustering.

Yuichiro Wada, Shugo Miyamoto, Takumi Nakagama, Léo Andéol, Wataru Kumagai, Takafumi Kanamori

Entropy, 2019

Model Description of Similarity-Based Recommendation Systems.

Takafumi Kanamori, Naoya Osugi

Entropy, 2019

Robust modal regression with direct log-density derivative estimation.

Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori

CoRR, 2019

Estimating Density Models with Complex Truncation Boundaries.

Song Liu, Takafumi Kanamori

CoRR, 2019

Unified estimation framework for unnormalized models with statistical efficiency.

Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi, Takeru Matsuda

CoRR, 2019

Fisher Efficient Inference of Intractable Models.

Song Liu, Takafumi Kanamori, Wittawat Jitkrittum, Yu Chen

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2017

Graph-based composite local Bregman divergences on discrete sample spaces.

Takafumi Kanamori, Takashi Takenouchi

Neural Networks, 2017

Robustness of learning algorithms using hinge loss with outlier indicators.

Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda

Neural Networks, 2017

DC Algorithm for Extended Robust Support Vector Machine.

Shuhei Fujiwara, Akiko Takeda, Takafumi Kanamori

Neural Comput., 2017

Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences.

Takashi Takenouchi, Takafumi Kanamori

J. Mach. Learn. Res., 2017

Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios.

Hiroaki Sasaki, Takafumi Kanamori, Aapo Hyvärinen, Gang Niu, Masashi Sugiyama

J. Mach. Learn. Res., 2017

Parallel distributed block coordinate descent methods based on pairwise comparison oracle.

Kota Matsui, Wataru Kumagai, Takafumi Kanamori

J. Glob. Optim., 2017

Breakdown Point of Robust Support Vector Machines.

Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda

Entropy, 2017

Estimating Density Ridges by Direct Estimation of Density-Derivative-Ratios.

Hiroaki Sasaki, Takafumi Kanamori, Masashi Sugiyama

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Efficiency Bound of Local Z-Estimators on Discrete Sample Spaces.

Takafumi Kanamori

Entropy, 2016

2015

Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces.

Takashi Takenouchi, Takafumi Kanamori

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Using financial risk measures for analyzing generalization performance of machine learning models.

Akiko Takeda, Takafumi Kanamori

Neural Networks, 2014

Extended Robust Support Vector Machine Based on Financial Risk Minimization.

Akiko Takeda, Shuhei Fujiwara, Takafumi Kanamori

Neural Comput., 2014

Constrained Least-Squares Density-Difference Estimation.

Tuan Duong Nguyen, Marthinus Christoffel du Plessis, Takafumi Kanamori, Masashi Sugiyama

IEICE Trans. Inf. Syst., 2014

Statistical Analysis of Distance Estimators with Density Differences and Density Ratios.

Takafumi Kanamori, Masashi Sugiyama

Entropy, 2014

Scale-Invariant Divergences for Density Functions.

Takafumi Kanamori

Entropy, 2014

Breakdown Point of Robust Support Vector Machine.

Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda

CoRR, 2014

Numerical study of learning algorithms on Stiefel manifold.

Takafumi Kanamori, Akiko Takeda

Comput. Manag. Sci., 2014

2013

A Bregman extension of quasi-Newton updates I: an information geometrical framework.

Takafumi Kanamori, Atsumi Ohara

Optim. Methods Softw., 2013

Relative Density-Ratio Estimation for Robust Distribution Comparison.

Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama

Neural Comput., 2013

A Unified Classification Model Based on Robust Optimization.

Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori

Neural Comput., 2013

Density-Difference Estimation.

Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, Ichiro Takeuchi

Neural Comput., 2013

Semi-supervised learning with density-ratio estimation.

Masanori Kawakita, Takafumi Kanamori

Mach. Learn., 2013

Computational complexity of kernel-based density-ratio estimation: a condition number analysis.

Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama

Mach. Learn., 2013

Conjugate relation between loss functions and uncertainty sets in classification problems.

Takafumi Kanamori, Akiko Takeda, Taiji Suzuki

J. Mach. Learn. Res., 2013

Direct Divergence Approximation between Probability Distributions and Its Applications in Machine Learning.

Masashi Sugiyama, Song Liu, Marthinus Christoffel du Plessis, Masao Yamanaka, Makoto Yamada, Taiji Suzuki, Takafumi Kanamori

J. Comput. Sci. Eng., 2013

A Bregman extension of quasi-Newton updates II: Analysis of robustness properties.

Takafumi Kanamori, Atsumi Ohara

J. Comput. Appl. Math., 2013

Improving Logitboost with prior knowledge.

Takafumi Kanamori, Takashi Takenouchi

Inf. Fusion, 2013

Statistical models and learning algorithms for ordinal regression problems.

Takafumi Kanamori

Inf. Fusion, 2013

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 analysis of kernel-based least-squares density-ratio estimation.

Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama

Mach. Learn., 2012

Worst-Case Violation of Sampled Convex Programs for Optimization with Uncertainty.

Takafumi Kanamori, Akiko Takeda

J. Optim. Theory Appl., 2012

A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems.

Takafumi Kanamori, Akiko Takeda, Taiji Suzuki

Proceedings of the COLT 2012, 2012

Non-convex Optimization on Stiefel Manifold and Applications to Machine Learning.

Takafumi Kanamori, Akiko Takeda

Proceedings of the Neural Information Processing - 19th International Conference, 2012

A Unified Robust Classification Model.

Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori

Proceedings of the 29th International Conference on Machine Learning, 2012

Density Ratio Estimation in Machine Learning.

Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori

Cambridge University Press, ISBN: 978-0-521-19017-6, 2012

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

Least-squares two-sample test.

Masashi Sugiyama, Taiji Suzuki, Yuta Itoh, Takafumi Kanamori, Manabu Kimura

Neural Networks, 2011

Statistical outlier detection using direct density ratio estimation.

Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, Takafumi Kanamori

Knowl. Inf. Syst., 2011

Multiscale Bagging and Its Applications.

Hidetoshi Shimodaira, Takafumi Kanamori, Masayoshi Aoki, Kouta Mine

IEICE Trans. Inf. Syst., 2011

2010

Deformation of log-likelihood loss function for multiclass boosting.

Takafumi Kanamori

Neural Networks, 2010

Conditional Density Estimation via Least-Squares Density Ratio Estimation.

Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Least-Squares Conditional Density Estimation.

Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara

IEICE Trans. Inf. Syst., 2010

Theoretical Analysis of Density Ratio Estimation.

Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama

IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2010

Direct Density Ratio Estimation with Dimensionality Reduction.

Masashi Sugiyama, Satoshi Hara, Paul von Bünau, Taiji Suzuki, Takafumi Kanamori, Motoaki Kawanabe

Proceedings of the SIAM International Conference on Data Mining, 2010

2009

Nonparametric Conditional Density Estimation Using Piecewise-Linear Solution Path of Kernel Quantile Regression.

Ichiro Takeuchi, Kaname Nomura, Takafumi Kanamori

Neural Comput., 2009

A Least-squares Approach to Direct Importance Estimation.

Takafumi Kanamori, Shohei Hido, Masashi Sugiyama

J. Mach. Learn. Res., 2009

A Density-ratio Framework for Statistical Data Processing.

Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Shohei Hido, Jun Sese, Ichiro Takeuchi, Liwei Wang

IPSJ Trans. Comput. Vis. Appl., 2009

A robust approach based on conditional value-at-risk measure to statistical learning problems.

Akiko Takeda, Takafumi Kanamori

Eur. J. Oper. Res., 2009

Mutual information estimation reveals global associations between stimuli and biological processes.

Taiji Suzuki, Masashi Sugiyama, Takafumi Kanamori, Jun Sese

BMC Bioinform., 2009

2008

Robust Boosting Algorithm Against Mislabeling in Multiclass Problems.

Takashi Takenouchi, Shinto Eguchi, Noboru Murata, Takafumi Kanamori

Neural Comput., 2008

Approximating Mutual Information by Maximum Likelihood Density Ratio Estimation.

Taiji Suzuki, Masashi Sugiyama, Jun Sese, Takafumi Kanamori

Proceedings of the Third Workshop on New Challenges for Feature Selection in Data Mining and Knowledge Discovery, 2008

Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection.

Takafumi Kanamori, Shohei Hido, Masashi Sugiyama

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Inlier-Based Outlier Detection via Direct Density Ratio Estimation.

Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, Takafumi Kanamori

Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 2008

2007

Robust Loss Functions for Boosting.

Takafumi Kanamori, Takashi Takenouchi, Shinto Eguchi, Noboru Murata

Neural Comput., 2007

Pool-based active learning with optimal sampling distribution and its information geometrical interpretation.

Takafumi Kanamori

Neurocomputing, 2007

Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability.

Takafumi Kanamori

IEICE Trans. Inf. Syst., 2007

2006

Geometrical Structure of Boosting Algorithm.

Takafumi Kanamori, Takashi Takenouchi, Noboru Murata

New Gener. Comput., 2006

Conditional mean estimation under asymmetric and heteroscedastic error by linear combination of quantile regressions.

Takafumi Kanamori, Ichiro Takeuchi

Comput. Stat. Data Anal., 2006

The Entire Solution Path of Kernel-based Nonparametric Conditional Quantile Estimator.

Ichiro Takeuchi, Kaname Nomura, Takafumi Kanamori

Proceedings of the International Joint Conference on Neural Networks, 2006

2004

Information Geometry of U-Boost and Bregman Divergence.

Noboru Murata, Takashi Takenouchi, Takafumi Kanamori, Shinto Eguchi

Neural Comput., 2004

The Most Robust Loss Function for Boosting.

Takafumi Kanamori, Takashi Takenouchi, Shinto Eguchi, Noboru Murata

Proceedings of the Neural Information Processing, 11th International Conference, 2004

2002

Robust Regression with Asymmetric Heavy-Tail Noise Distributions.

Ichiro Takeuchi, Yoshua Bengio, Takafumi Kanamori

Neural Comput., 2002

A New Sequential Algorithm for Regression Problems by Using Mixture Distribution.

Takafumi Kanamori

Proceedings of the Artificial Neural Networks, 2002