Takafumi Kanamori

Publication Activity (10 Years)

Years Active: 2002-2024
Publications (10 Years): 36

Top Topics

Support Vector Machine

Efficient Inference

Risk Minimization

Invariant Representations

Top Venues

Neural Networks

Publications

Hiroo Irobe, Wataru Aoki, Kimihiro Yamazaki, Yuhui Zhang, Takumi Nakagawa, Hiroki Waida, Yuichiro Wada, Takafumi Kanamori
Robust VAEs via Generating Process of Noise Augmented Data. CoRR (2024)
Takumi Nakagawa, Yutaro Sanada, Hiroki Waida, Yuhui Zhang, Yuichiro Wada, Kosaku Takanashi, Tomonori Yamada, Takafumi Kanamori
Denoising cosine similarity: A theory-driven approach for efficient representation learning. Neural Networks 169 (2024)
Yuhui Zhang, Yuichiro Wada, Hiroki Waida, Kaito Goto, Yusaku Hino, Takafumi Kanamori
Deep Clustering with a Constraint for Topological Invariance based on Symmetric InfoNCE. CoRR (2023)
Takumi Nakagawa, Yutaro Sanada, Hiroki Waida, Yuhui Zhang, Yuichiro Wada, Kosaku Takanashi, Tomonori Yamada, Takafumi Kanamori
Denoising Cosine Similarity: A Theory-Driven Approach for Efficient Representation Learning. CoRR (2023)
Hiroki Waida, Yuichiro Wada, Léo Andéol, Takumi Nakagawa, Yuhui Zhang, Takafumi Kanamori
Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis. CoRR (2023)
Léo Andéol, Yusei Kawakami, Yuichiro Wada, Takafumi Kanamori, Klaus-Robert Müller, Grégoire Montavon
Learning domain invariant representations by joint Wasserstein distance minimization. Neural Networks 167 (2023)
Yuhui Zhang, Yuichiro Wada, Hiroki Waida, Kaito Goto, Yusaku Hino, Takafumi Kanamori
Deep Clustering With a Constraint for Topological Invariance Based on Symmetric InfoNCE. Neural Comput. 35 (7) (2023)
Hiroki Waida, Yuichiro Wada, Léo Andéol, Takumi Nakagawa, Yuhui Zhang, Takafumi Kanamori
Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis. ECML/PKDD (4) (2023)
Kei Ishikawa, Niao He, Takafumi Kanamori
A Convex Framework for Confounding Robust Inference. CoRR (2023)
Hiroaki Sasaki, Jun-Ichiro Hirayama, Takafumi Kanamori
Mode estimation on matrix manifolds: Convergence and robustness. AISTATS (2022)
Yutaro Sanada, Takumi Nakagawa, Yuichiro Wada, Kosaku Takanashi, Yuhui Zhang, Kiichi Tokuyama, Takafumi Kanamori, Tomonori Yamada
Deep Self-Supervised Learning of Speech Denoising from Noisy Speeches. INTERSPEECH (2022)
Song Liu, Takafumi Kanamori, Daniel J. Williams
Estimating Density Models with Truncation Boundaries using Score Matching. J. Mach. Learn. Res. 23 (2022)
Yuki Mae, Wataru Kumagai, Takafumi Kanamori
Uncertainty propagation for dropout-based Bayesian neural networks. Neural Networks 144 (2021)
Léo Andéol, Yusei Kawakami, Yuichiro Wada, Takafumi Kanamori, Klaus-Robert Müller, Grégoire Montavon
Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization. CoRR (2021)
Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori
Robust modal regression with direct gradient approximation of modal regression risk. UAI (2020)
Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi, Takeru Matsuda
A Unified Statistically Efficient Estimation Framework for Unnormalized Models. AISTATS (2020)
Yuichiro Wada, Shugo Miyamoto, Takumi Nakagama, Léo Andéol, Wataru Kumagai, Takafumi Kanamori
Spectral Embedded Deep Clustering. Entropy 21 (8) (2019)
Song Liu, Takafumi Kanamori, Wittawat Jitkrittum, Yu Chen
Fisher Efficient Inference of Intractable Models. NeurIPS (2019)
Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori
Robust modal regression with direct log-density derivative estimation. CoRR (2019)
Song Liu, Takafumi Kanamori
Estimating Density Models with Complex Truncation Boundaries. CoRR (2019)
Yuichiro Wada, Siqiang Su, Wataru Kumagai, Takafumi Kanamori
-Nearest Neighbor in Online Semi-Supervised Learning. IEICE Trans. Inf. Syst. (8) (2019)
Takafumi Kanamori, Naoya Osugi
Model Description of Similarity-Based Recommendation Systems. Entropy 21 (7) (2019)
Kota Matsui, Wataru Kumagai, Kenta Kanamori, Mitsuaki Nishikimi, Takafumi Kanamori
Variable Selection for Nonparametric Learning with Power Series Kernels. Neural Comput. 31 (8) (2019)
Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi, Takeru Matsuda
Unified estimation framework for unnormalized models with statistical efficiency. CoRR (2019)
Wataru Kumagai, Takafumi Kanamori
Risk bound of transfer learning using parametric feature mapping and its application to sparse coding. Mach. Learn. 108 (11) (2019)
Kota Matsui, Wataru Kumagai, Kenta Kanamori, Mitsuaki Nishikimi, Takafumi Kanamori
Variable Selection for Nonparametric Learning with Power Series Kernels. CoRR (2018)
Hiroaki Sasaki, Takafumi Kanamori, Masashi Sugiyama
Estimating Density Ridges by Direct Estimation of Density-Derivative-Ratios. AISTATS (2017)
Kota Matsui, Wataru Kumagai, Takafumi Kanamori
Parallel distributed block coordinate descent methods based on pairwise comparison oracle. J. Glob. Optim. 69 (1) (2017)
Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda
Breakdown Point of Robust Support Vector Machines. Entropy 19 (2) (2017)
Takashi Takenouchi, Takafumi Kanamori
Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences. J. Mach. Learn. Res. 18 (2017)
Takafumi Kanamori, Takashi Takenouchi
Graph-based composite local Bregman divergences on discrete sample spaces. Neural Networks 95 (2017)
Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda
Robustness of learning algorithms using hinge loss with outlier indicators. Neural Networks 94 (2017)
Shuhei Fujiwara, Akiko Takeda, Takafumi Kanamori
DC Algorithm for Extended Robust Support Vector Machine. Neural Comput. 29 (5) (2017)
Hiroaki Sasaki, Takafumi Kanamori, Aapo Hyvärinen, Gang Niu, Masashi Sugiyama
Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios. J. Mach. Learn. Res. 18 (2017)
Takafumi Kanamori
Efficiency Bound of Local Z-Estimators on Discrete Sample Spaces. Entropy 18 (7) (2016)
Takashi Takenouchi, Takafumi Kanamori
Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces. NIPS (2015)
Takafumi Kanamori
Scale-Invariant Divergences for Density Functions. Entropy 16 (5) (2014)
Takafumi Kanamori, Akiko Takeda
Numerical study of learning algorithms on Stiefel manifold. Comput. Manag. Sci. 11 (4) (2014)
Takafumi Kanamori, Masashi Sugiyama
Statistical Analysis of Distance Estimators with Density Differences and Density Ratios. Entropy 16 (2) (2014)
Akiko Takeda, Takafumi Kanamori
Using financial risk measures for analyzing generalization performance of machine learning models. Neural Networks 57 (2014)
Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda
Breakdown Point of Robust Support Vector Machine. CoRR (2014)
Akiko Takeda, Shuhei Fujiwara, Takafumi Kanamori
Extended Robust Support Vector Machine Based on Financial Risk Minimization. Neural Comput. 26 (11) (2014)
Kota Matsui, Wataru Kumagai, Takafumi Kanamori
Parallel Distributed Block Coordinate Descent Methods based on Pairwise Comparison Oracle. CoRR (2014)
Tuan Duong Nguyen, Marthinus Christoffel du Plessis, Takafumi Kanamori, Masashi Sugiyama
Constrained Least-Squares Density-Difference Estimation. IEICE Trans. Inf. Syst. (7) (2014)
Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama
Computational complexity of kernel-based density-ratio estimation: a condition number analysis. Mach. Learn. 90 (3) (2013)
Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama
Relative Density-Ratio Estimation for Robust Distribution Comparison. Neural Comput. 25 (5) (2013)
Masanori Kawakita, Takafumi Kanamori
Semi-supervised learning with density-ratio estimation. Mach. Learn. 91 (2) (2013)
Takafumi Kanamori, Atsumi Ohara
A Bregman extension of quasi-Newton updates II: Analysis of robustness properties. J. Comput. Appl. Math. 253 (2013)
Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, Ichiro Takeuchi
Density-Difference Estimation. Neural Comput. 25 (10) (2013)
Takafumi Kanamori
Statistical models and learning algorithms for ordinal regression problems. Inf. Fusion 14 (2) (2013)
Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori
A Unified Classification Model Based on Robust Optimization. Neural Comput. 25 (3) (2013)
Masashi Sugiyama, Song Liu, Marthinus Christoffel du Plessis, Masao Yamanaka, Makoto Yamada, Taiji Suzuki, Takafumi Kanamori
Direct Divergence Approximation between Probability Distributions and Its Applications in Machine Learning. J. Comput. Sci. Eng. 7 (2) (2013)
Takafumi Kanamori, Takashi Takenouchi
Improving Logitboost with prior knowledge. Inf. Fusion 14 (2) (2013)
Takafumi Kanamori, Atsumi Ohara
A Bregman extension of quasi-Newton updates I: an information geometrical framework. Optim. Methods Softw. 28 (1) (2013)
Takafumi Kanamori, Akiko Takeda, Taiji Suzuki
Conjugate relation between loss functions and uncertainty sets in classification problems. J. Mach. Learn. Res. 14 (1) (2013)
Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama
f-Divergence Estimation and Two-Sample Homogeneity Test Under Semiparametric Density-Ratio Models. IEEE Trans. Inf. Theory 58 (2) (2012)
Takafumi Kanamori, Akiko Takeda
Worst-Case Violation of Sampled Convex Programs for Optimization with Uncertainty. J. Optim. Theory Appl. 152 (1) (2012)
Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, Ichiro Takeuchi
Density-Difference Estimation CoRR (2012)
Takafumi Kanamori, Akiko Takeda, Taiji Suzuki
A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems. COLT (2012)
Takafumi Kanamori, Akiko Takeda
Non-convex Optimization on Stiefel Manifold and Applications to Machine Learning. ICONIP (1) (2012)
Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, Ichiro Takeuchi
Density-Difference Estimation. NIPS (2012)
Takafumi Kanamori, Akiko Takeda, Taiji Suzuki
A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems CoRR (2012)
Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori
A Unified Robust Classification Model. ICML (2012)
Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama
Statistical analysis of kernel-based least-squares density-ratio estimation. Mach. Learn. 86 (3) (2012)
Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori
A Unified Robust Classification Model CoRR (2012)
Masashi Sugiyama, Taiji Suzuki, Yuta Itoh, Takafumi Kanamori, Manabu Kimura
Least-squares two-sample test. Neural Networks 24 (7) (2011)
Masashi Sugiyama, Makoto Yamada, Paul von Bünau, Taiji Suzuki, Takafumi Kanamori, Motoaki Kawanabe
Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search. Neural Networks 24 (2) (2011)
Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, Takafumi Kanamori
Statistical outlier detection using direct density ratio estimation. Knowl. Inf. Syst. 26 (2) (2011)
Hidetoshi Shimodaira, Takafumi Kanamori, Masayoshi Aoki, Kouta Mine
Multiscale Bagging and Its Applications. IEICE Trans. Inf. Syst. (10) (2011)
Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama
Relative Density-Ratio Estimation for Robust Distribution Comparison. NIPS (2011)
Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama
Theoretical Analysis of Density Ratio Estimation. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. (4) (2010)
Masashi Sugiyama, Satoshi Hara, Paul von Bünau, Taiji Suzuki, Takafumi Kanamori, Motoaki Kawanabe
Direct Density Ratio Estimation with Dimensionality Reduction. SDM (2010)
Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara
Least-Squares Conditional Density Estimation. IEICE Trans. Inf. Syst. (3) (2010)
Takafumi Kanamori
Deformation of log-likelihood loss function for multiclass boosting. Neural Networks 23 (7) (2010)
Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara
Conditional Density Estimation via Least-Squares Density Ratio Estimation. AISTATS (2010)
Takafumi Kanamori, Shohei Hido, Masashi Sugiyama
A Least-squares Approach to Direct Importance Estimation. J. Mach. Learn. Res. 10 (2009)
Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Shohei Hido, Jun Sese, Ichiro Takeuchi, Liwei Wang
A Density-ratio Framework for Statistical Data Processing. IPSJ Trans. Comput. Vis. Appl. 1 (2009)
Taiji Suzuki, Masashi Sugiyama, Takafumi Kanamori, Jun Sese
Mutual information estimation reveals global associations between stimuli and biological processes. BMC Bioinform. 10 (S-1) (2009)
Akiko Takeda, Takafumi Kanamori
A robust approach based on conditional value-at-risk measure to statistical learning problems. Eur. J. Oper. Res. 198 (1) (2009)
Ichiro Takeuchi, Kaname Nomura, Takafumi Kanamori
Nonparametric Conditional Density Estimation Using Piecewise-Linear Solution Path of Kernel Quantile Regression. Neural Comput. 21 (2) (2009)
Takashi Takenouchi, Shinto Eguchi, Noboru Murata, Takafumi Kanamori
Robust Boosting Algorithm Against Mislabeling in Multiclass Problems. Neural Comput. 20 (6) (2008)
Taiji Suzuki, Masashi Sugiyama, Jun Sese, Takafumi Kanamori
Approximating Mutual Information by Maximum Likelihood Density Ratio Estimation. FSDM (2008)
Takafumi Kanamori, Shohei Hido, Masashi Sugiyama
Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection. NIPS (2008)
Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, Takafumi Kanamori
Inlier-Based Outlier Detection via Direct Density Ratio Estimation. ICDM (2008)
Takafumi Kanamori
Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability. IEICE Trans. Inf. Syst. (12) (2007)
Takafumi Kanamori
Pool-based active learning with optimal sampling distribution and its information geometrical interpretation. Neurocomputing 71 (1-3) (2007)
Takafumi Kanamori
Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability. ALT (2007)
Takafumi Kanamori, Takashi Takenouchi, Shinto Eguchi, Noboru Murata
Robust Loss Functions for Boosting. Neural Comput. 19 (8) (2007)
Takafumi Kanamori, Takashi Takenouchi, Noboru Murata
Geometrical Structure of Boosting Algorithm. New Gener. Comput. 25 (1) (2006)
Takafumi Kanamori, Ichiro Takeuchi
Conditional mean estimation under asymmetric and heteroscedastic error by linear combination of quantile regressions. Comput. Stat. Data Anal. 50 (12) (2006)
Ichiro Takeuchi, Kaname Nomura, Takafumi Kanamori
The Entire Solution Path of Kernel-based Nonparametric Conditional Quantile Estimator. IJCNN (2006)
Takafumi Kanamori, Takashi Takenouchi, Shinto Eguchi, Noboru Murata
The Most Robust Loss Function for Boosting. ICONIP (2004)
Noboru Murata, Takashi Takenouchi, Takafumi Kanamori, Shinto Eguchi
Information Geometry of U-Boost and Bregman Divergence. Neural Comput. 16 (7) (2004)
Takafumi Kanamori
A New Sequential Algorithm for Regression Problems by Using Mixture Distribution. ICANN (2002)
Ichiro Takeuchi, Yoshua Bengio, Takafumi Kanamori
Robust Regression with Asymmetric Heavy-Tail Noise Distributions. Neural Comput. 14 (10) (2002)