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ORCIDTakafumi Kanamori
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2020 – today
- 2024
[j59]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: 226-241 (2024)- [c23]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. ISIT 2024: 587-592 - [i15]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 abs/2407.18632 (2024) - 2023
- [j58]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): 1288-1339 (2023) - [j57]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: 233-243 (2023) - [c22]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: 709-727 - [i14]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 abs/2303.03036 (2023) - [i13]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 abs/2304.00395 (2023) - [i12]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 abs/2304.09552 (2023) - [i11]Kei Ishikawa, Niao He, Takafumi Kanamori:
A Convex Framework for Confounding Robust Inference. CoRR abs/2309.12450 (2023) - 2022
- [j56]Song Liu, Takafumi Kanamori, Daniel J. Williams:
Estimating Density Models with Truncation Boundaries using Score Matching. J. Mach. Learn. Res. 23: 186:1-186:38 (2022) - [c21]Hiroaki Sasaki, Jun-Ichiro Hirayama, Takafumi Kanamori:
Mode estimation on matrix manifolds: Convergence and robustness. AISTATS 2022: 8056-8079 - [c20]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: 1178-1182 - 2021
- [j55]Yuki Mae, Wataru Kumagai, Takafumi Kanamori:
Uncertainty propagation for dropout-based Bayesian neural networks. Neural Networks 144: 394-406 (2021) - [i10]Léo Andéol, Yusei Kawakami, Yuichiro Wada, Takafumi Kanamori, Klaus-Robert Müller, Grégoire Montavon:
Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization. CoRR abs/2106.04923 (2021) - 2020
- [c19]Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi, Takeru Matsuda:
A Unified Statistically Efficient Estimation Framework for Unnormalized Models. AISTATS 2020: 809-819 - [c18]Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori:
Robust modal regression with direct gradient approximation of modal regression risk. UAI 2020: 380-389
2010 – 2019
- 2019
- [j54]Takafumi Kanamori, Naoya Osugi:
Model Description of Similarity-Based Recommendation Systems. Entropy 21(7): 702 (2019) - [j53]Yuichiro Wada, Shugo Miyamoto, Takumi Nakagama, Léo Andéol, Wataru Kumagai, Takafumi Kanamori:
Spectral Embedded Deep Clustering. Entropy 21(8): 795 (2019) - [j52]Yuichiro Wada, Siqiang Su, Wataru Kumagai, Takafumi Kanamori:
Robust Label Prediction via Label Propagation and Geodesic k-Nearest Neighbor in Online Semi-Supervised Learning. IEICE Trans. Inf. Syst. 102-D(8): 1537-1545 (2019) - [j51]Wataru Kumagai, Takafumi Kanamori:
Risk bound of transfer learning using parametric feature mapping and its application to sparse coding. Mach. Learn. 108(11): 1975-2008 (2019) - [j50]Kota Matsui, Wataru Kumagai, Kenta Kanamori, Mitsuaki Nishikimi, Takafumi Kanamori:
Variable Selection for Nonparametric Learning with Power Series Kernels. Neural Comput. 31(8): 1718-1750 (2019) - [c17]Song Liu, Takafumi Kanamori, Wittawat Jitkrittum, Yu Chen:
Fisher Efficient Inference of Intractable Models. NeurIPS 2019: 8790-8800 - [i9]Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi, Takeru Matsuda:
Unified estimation framework for unnormalized models with statistical efficiency. CoRR abs/1901.07710 (2019) - [i8]Song Liu, Takafumi Kanamori:
Estimating Density Models with Complex Truncation Boundaries. CoRR abs/1910.03834 (2019) - [i7]Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori:
Robust modal regression with direct log-density derivative estimation. CoRR abs/1910.08280 (2019) - 2018
- [i6]Kota Matsui, Wataru Kumagai, Kenta Kanamori, Mitsuaki Nishikimi, Takafumi Kanamori:
Variable Selection for Nonparametric Learning with Power Series Kernels. CoRR abs/1806.00569 (2018) - 2017
- [j49]Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda:
Breakdown Point of Robust Support Vector Machines. Entropy 19(2): 83 (2017) - [j48]Kota Matsui, Wataru Kumagai, Takafumi Kanamori:
Parallel distributed block coordinate descent methods based on pairwise comparison oracle. J. Glob. Optim. 69(1): 1-21 (2017) - [j47]Takashi Takenouchi, Takafumi Kanamori:
Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences. J. Mach. Learn. Res. 18: 56:1-56:26 (2017) - [j46]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: 180:1-180:47 (2017) - [j45]Shuhei Fujiwara, Akiko Takeda, Takafumi Kanamori:
DC Algorithm for Extended Robust Support Vector Machine. Neural Comput. 29(5): 1406-1438 (2017) - [j44]Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda:
Robustness of learning algorithms using hinge loss with outlier indicators. Neural Networks 94: 173-191 (2017) - [j43]Takafumi Kanamori, Takashi Takenouchi:
Graph-based composite local Bregman divergences on discrete sample spaces. Neural Networks 95: 44-56 (2017) - [c16]Hiroaki Sasaki, Takafumi Kanamori, Masashi Sugiyama:
Estimating Density Ridges by Direct Estimation of Density-Derivative-Ratios. AISTATS 2017: 204-212 - 2016
- [j42]Takafumi Kanamori:
Efficiency Bound of Local Z-Estimators on Discrete Sample Spaces. Entropy 18(7): 273 (2016) - 2015
- [c15]Takashi Takenouchi, Takafumi Kanamori:
Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces. NIPS 2015: 820-828 - 2014
- [j41]Takafumi Kanamori, Akiko Takeda:
Numerical study of learning algorithms on Stiefel manifold. Comput. Manag. Sci. 11(4): 319-340 (2014) - [j40]Takafumi Kanamori, Masashi Sugiyama:
Statistical Analysis of Distance Estimators with Density Differences and Density Ratios. Entropy 16(2): 921-942 (2014) - [j39]Takafumi Kanamori:
Scale-Invariant Divergences for Density Functions. Entropy 16(5): 2611-2628 (2014) - [j38]Tuan Duong Nguyen, Marthinus Christoffel du Plessis, Takafumi Kanamori, Masashi Sugiyama:
Constrained Least-Squares Density-Difference Estimation. IEICE Trans. Inf. Syst. 97-D(7): 1822-1829 (2014) - [j37]Akiko Takeda, Shuhei Fujiwara, Takafumi Kanamori:
Extended Robust Support Vector Machine Based on Financial Risk Minimization. Neural Comput. 26(11): 2541-2569 (2014) - [j36]Akiko Takeda, Takafumi Kanamori:
Using financial risk measures for analyzing generalization performance of machine learning models. Neural Networks 57: 29-38 (2014) - [i5]Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda:
Breakdown Point of Robust Support Vector Machine. CoRR abs/1409.0934 (2014) - [i4]Kota Matsui, Wataru Kumagai, Takafumi Kanamori:
Parallel Distributed Block Coordinate Descent Methods based on Pairwise Comparison Oracle. CoRR abs/1409.3912 (2014) - 2013
- [j35]Takafumi Kanamori:
Statistical models and learning algorithms for ordinal regression problems. Inf. Fusion 14(2): 199-207 (2013) - [j34]Takafumi Kanamori, Takashi Takenouchi:
Improving Logitboost with prior knowledge. Inf. Fusion 14(2): 208-219 (2013) - [j33]Takafumi Kanamori, Atsumi Ohara:
A Bregman extension of quasi-Newton updates II: Analysis of robustness properties. J. Comput. Appl. Math. 253: 104-122 (2013) - [j32]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): 99-111 (2013) - [j31]Takafumi Kanamori, Akiko Takeda, Taiji Suzuki:
Conjugate relation between loss functions and uncertainty sets in classification problems. J. Mach. Learn. Res. 14(1): 1461-1504 (2013) - [j30]Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama:
Computational complexity of kernel-based density-ratio estimation: a condition number analysis. Mach. Learn. 90(3): 431-460 (2013) - [j29]Masanori Kawakita, Takafumi Kanamori:
Semi-supervised learning with density-ratio estimation. Mach. Learn. 91(2): 189-209 (2013) - [j28]Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori:
A Unified Classification Model Based on Robust Optimization. Neural Comput. 25(3): 759-804 (2013) - [j27]Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama:
Relative Density-Ratio Estimation for Robust Distribution Comparison. Neural Comput. 25(5): 1324-1370 (2013) - [j26]Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, Ichiro Takeuchi:
Density-Difference Estimation. Neural Comput. 25(10): 2734-2775 (2013) - [j25]Takafumi Kanamori, Atsumi Ohara:
A Bregman extension of quasi-Newton updates I: an information geometrical framework. Optim. Methods Softw. 28(1): 96-123 (2013) - 2012
- [b1]Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori:
Density Ratio Estimation in Machine Learning. Cambridge University Press 2012, ISBN 978-0-521-19017-6, pp. I-XII, 1-329 - [j24]Takafumi Kanamori, Akiko Takeda:
Worst-Case Violation of Sampled Convex Programs for Optimization with Uncertainty. J. Optim. Theory Appl. 152(1): 171-197 (2012) - [j23]Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama:
Statistical analysis of kernel-based least-squares density-ratio estimation. Mach. Learn. 86(3): 335-367 (2012) - [j22]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): 708-720 (2012) - [c14]Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori:
A Unified Robust Classification Model. ICML 2012 - [c13]Takafumi Kanamori, Akiko Takeda:
Non-convex Optimization on Stiefel Manifold and Applications to Machine Learning. ICONIP (1) 2012: 109-116 - [c12]Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, Ichiro Takeuchi:
Density-Difference Estimation. NIPS 2012: 692-700 - [c11]Takafumi Kanamori, Akiko Takeda, Taiji Suzuki:
A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems. COLT 2012: 29.1-29.23 - [i3]Takafumi Kanamori, Akiko Takeda, Taiji Suzuki:
A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems. CoRR abs/1204.6583 (2012) - [i2]Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori:
A Unified Robust Classification Model. CoRR abs/1206.4599 (2012) - [i1]Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, Ichiro Takeuchi:
Density-Difference Estimation. CoRR abs/1207.0099 (2012) - 2011
- [j21]Hidetoshi Shimodaira, Takafumi Kanamori, Masayoshi Aoki, Kouta Mine:
Multiscale Bagging and Its Applications. IEICE Trans. Inf. Syst. 94-D(10): 1924-1932 (2011) - [j20]Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, Takafumi Kanamori:
Statistical outlier detection using direct density ratio estimation. Knowl. Inf. Syst. 26(2): 309-336 (2011) - [j19]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): 183-198 (2011) - [j18]Masashi Sugiyama, Taiji Suzuki, Yuta Itoh, Takafumi Kanamori, Manabu Kimura:
Least-squares two-sample test. Neural Networks 24(7): 735-751 (2011) - [c10]Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama:
Relative Density-Ratio Estimation for Robust Distribution Comparison. NIPS 2011: 594-602 - 2010
- [j17]Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara:
Least-Squares Conditional Density Estimation. IEICE Trans. Inf. Syst. 93-D(3): 583-594 (2010) - [j16]Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama:
Theoretical Analysis of Density Ratio Estimation. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 93-A(4): 787-798 (2010) - [j15]Takafumi Kanamori:
Deformation of log-likelihood loss function for multiclass boosting. Neural Networks 23(7): 843-864 (2010) - [c9]Masashi Sugiyama, Satoshi Hara, Paul von Bünau, Taiji Suzuki, Takafumi Kanamori, Motoaki Kawanabe:
Direct Density Ratio Estimation with Dimensionality Reduction. SDM 2010: 595-606 - [c8]Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara:
Conditional Density Estimation via Least-Squares Density Ratio Estimation. AISTATS 2010: 781-788
2000 – 2009
- 2009
- [j14]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) - [j13]Akiko Takeda, Takafumi Kanamori:
A robust approach based on conditional value-at-risk measure to statistical learning problems. Eur. J. Oper. Res. 198(1): 287-296 (2009) - [j12]Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Shohei Hido, Jun Sese, Ichiro Takeuchi, Liwei Wang:
A Density-ratio Framework for Statistical Data Processing. Inf. Media Technol. 4(4): 962-987 (2009) - [j11]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: 183-208 (2009) - [j10]Takafumi Kanamori, Shohei Hido, Masashi Sugiyama:
A Least-squares Approach to Direct Importance Estimation. J. Mach. Learn. Res. 10: 1391-1445 (2009) - [j9]Ichiro Takeuchi, Kaname Nomura, Takafumi Kanamori:
Nonparametric Conditional Density Estimation Using Piecewise-Linear Solution Path of Kernel Quantile Regression. Neural Comput. 21(2): 533-559 (2009) - 2008
- [j8]Takashi Takenouchi, Shinto Eguchi, Noboru Murata, Takafumi Kanamori:
Robust Boosting Algorithm Against Mislabeling in Multiclass Problems. Neural Comput. 20(6): 1596-1630 (2008) - [c7]Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, Takafumi Kanamori:
Inlier-Based Outlier Detection via Direct Density Ratio Estimation. ICDM 2008: 223-232 - [c6]Takafumi Kanamori, Shohei Hido, Masashi Sugiyama:
Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection. NIPS 2008: 809-816 - [c5]Taiji Suzuki, Masashi Sugiyama, Jun Sese, Takafumi Kanamori:
Approximating Mutual Information by Maximum Likelihood Density Ratio Estimation. FSDM 2008: 5-20 - 2007
- [j7]Takafumi Kanamori:
Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability. IEICE Trans. Inf. Syst. 90-D(12): 2033-2042 (2007) - [j6]Takafumi Kanamori:
Pool-based active learning with optimal sampling distribution and its information geometrical interpretation. Neurocomputing 71(1-3): 353-362 (2007) - [j5]Takafumi Kanamori, Takashi Takenouchi, Shinto Eguchi, Noboru Murata:
Robust Loss Functions for Boosting. Neural Comput. 19(8): 2183-2244 (2007) - [c4]Takafumi Kanamori:
Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability. ALT 2007: 358-372 - 2006
- [j4]Takafumi Kanamori, Ichiro Takeuchi:
Conditional mean estimation under asymmetric and heteroscedastic error by linear combination of quantile regressions. Comput. Stat. Data Anal. 50(12): 3605-3618 (2006) - [j3]Takafumi Kanamori, Takashi Takenouchi, Noboru Murata:
Geometrical Structure of Boosting Algorithm. New Gener. Comput. 25(1): 117-141 (2006) - [c3]Ichiro Takeuchi, Kaname Nomura, Takafumi Kanamori:
The Entire Solution Path of Kernel-based Nonparametric Conditional Quantile Estimator. IJCNN 2006: 153-158 - 2004
- [j2]Noboru Murata, Takashi Takenouchi, Takafumi Kanamori, Shinto Eguchi:
Information Geometry of U-Boost and Bregman Divergence. Neural Comput. 16(7): 1437-1481 (2004) - [c2]Takafumi Kanamori, Takashi Takenouchi, Shinto Eguchi, Noboru Murata:
The Most Robust Loss Function for Boosting. ICONIP 2004: 496-501 - 2002
- [j1]Ichiro Takeuchi, Yoshua Bengio, Takafumi Kanamori:
Robust Regression with Asymmetric Heavy-Tail Noise Distributions. Neural Comput. 14(10): 2469-2496 (2002) - [c1]Takafumi Kanamori:
A New Sequential Algorithm for Regression Problems by Using Mixture Distribution. ICANN 2002: 535-540
Coauthor Index
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