論文

• Hiroaki Sasaki, Takafumi Kanamori, Aapo Hyvarinen, Masashi Sugiyama. Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios, Journal of Machine Learning Research, Vol. 18, pp. 1--47, Apr. 2018.
• Takafumi Kanamori, Takashi Takenouchi. Graph-based Composite Local Bregman Divergences on Discrete Sample Spaces, Neural Networks, Vol. 95, pp. 44--56, Nov. 2017.
• Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda. Robustness of Learning Algorithms using Hinge Loss with Outlier Indicators, Neural Networks, Vol. 94, pp. 173--191, Oct. 2017.
• Kota Matsui, Wataru Kumagai, Takafumi Kanamori. Parallel Distributed Block Coordinate Descent Methods based on Pairwise Comparison Oracle, Journal of Global Optimization, Vol. 69, pp. 1–21, Sept. 2017.
• Takashi Takenouchi, T. Kanamori. Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences, Journal of Machine Learning Research, Vol. 18, num. 56, pp. 1--26, July 2017.
• Shuhei Fujiwara, Akiko Takeda, Takafumi Kanamori. DC Algorithm for Extended Robust Support Vector Machine, Neural Computation, vol. 29, num. 5, pp. 1406--1438, May 2017.
• Yamada, M., Suzuki, T., Kanamori, T., Hachiya, H., Sugiyama, M.. Relatiive density-ratio estimationfor robust distribution comparision, Neural Computation, vol. 25, no. 5, pp. 1324-1370, 2013.
• Kanamori, Suzuki, T., Sugiyama, M.. Computational complexity of kernel-based density-ratio estimation:A condition number analysis, Machine Learning, vol. 90, no. 3, pp. 431-460, 2013.
• Sugiyama, M., Suzuki, T., Kanamori, T., Du Plessis, M.C., Liu, S., & Takeuchi, I.. Density-difference estimation, Neural Computation, vol. 25, no. 10, pp. 2734-2775, 2013.
• Sugiyama, M., Liu, S., Du Plessis, M.C., Yamanaka, M., Yamada, M., Suzuki, T., & Kanamori, T.. Direct divergence approximationbetween probability distributions and its applications inmachine learning, Journal of Computing Science and Engineering, vol. 7, no. 2, pp. 99-111, 2013.
• Kanamori, T., Suzuki, T., Sugiyama, M.. Statistical analisis of kernel-based least-squares density-ratio estimation, Machine Learning, vol. 86, no. 3, pp. 335-367, Mar. 2012.
• Sugiyama, M., Suzuki, T., Kanamori, T. Density ratio matching under the Bregman divergence:A unified framework of density ratio estimation, Annals of the Institlute of Statistical Mathematics, vol. 64, no. 5, pp. 1009-1044, 2012.
• Kanamori, T., Suzuki, T., Sugiyama, M.. F-divergence estimation and two-sample homogeneity test under semiparametric density-ratio models, IEEE Transactions on Information Theory, vol. 58, no. 2, pp. 708-720, 2012.
• Kanamori, T., Suzuki, T., Sugiyama, M.. Theoretical analysis of density ratio estimation, IEICE Transactions on Fundamentals of Electronics，Communication and Computer Sciences，, Vol. E-93-A, no. 4, pp. 787-798, 2010.
• Hido, S., Tsuboi, Y., Kashima, H., Sugiyama, M., Kanamori, T.. Statistical outlier detection using direct density ratio estimation., Knowledge and Information Systems, Vol. xxx, no. xxx, pp. xxx－xxx, 2010.
• Sugiyama, M., Takeuchi, I., Kanamori, T., Suzuki, T., Hachiya, H., Okanohara, D.. Least-squares conditional density estimation., IEICE Transactions on Infromation and Systems,, IEICE, Vol. E93-D, no. 3, pp. 583-594, 2010.
• Kanamori, T., Hido, S., Sugiyama, M.. A least-squares approach to direct importance estimation., Journal of Machine Learning Research, vol. 10(Jul), pp. 1391-1445, 2009.
• Sugiyama, M., Kanamori, T., Suzuki, T., Hido, S., Sese, J., Takeuchi, I., Wang, L.. A density-ratio framework for statistical data processing., IPSJ Transactions on Computer Vision and Applications,, vol. xxx, no. xxx, pp. 183-208, 2009.
• 村田昇, 金森敬文, 竹之内高志. ブースティングと学習アルゴリズム, 電子情報通信学会誌, No. 9, pp. 724-729, Sept. 2005.
• Takafumi Kanamori, Ichiro Takeuchi. Conditional mean estimation under asymmetric and heteroscedastic error by linear combination of quantile regressions, Computational Statistics & Data Analysis, Vol. -, No. -, pp. -, 2005.
• Takafumi KANAMORI. Robust Boosting and Loss Functions, IEICE technical Report, Vol. 104, No. 225, pp. 1-6, 2004.
• Takafumi KANAMORI. The most robust loss function for boosting, Lecture Notes in Computer Science Neural Information Processing, Vol. 3316, pp. 496-501, 2004.
• Murata, N., Takenouchi, T., Kanamori, T., Eguchi, S.. Information Geometry of U-Boost and Bregman Divergence, Neural Computation, Vol. 16, No. 7, pp. 1437-1482, 2004.
• Takafumi KANAMORI. Recent Development of Ensemble Learning, SICE symposium on Intelligent Systems, Vol. 31, pp. 25-30, 2004.
• Takafumi KANAMORI. Statistical Models for Multi-Class Classification and Integrability of Estimation Equations, Proceedings of Information-Based Induction Sciences, Vol. 7, pp. 170-177, 2004.
• Takafumi KANAMORI. Integrability of moment methos and loss functions for multi-class classification, Proceedings of JSS, pp. 406-407, 2004.
• Takafumi KANAMORI. Estimation of pure premium damage of damage insurance by using quantile regressions, Proceedings of JSS, pp. 199-100, 2004.
• Takafumi KANAMORI. Estimation of conditional mean by the linear combination of quantile regression under heteroscedastic asymmetric errors, IEICE technical Report, Vol. 103, No. 227, pp. 43-48, 2003.
• Taiafumi Kanamori, Hidetoshi Shimodaira. Active Learning algorithm using the maximum weighted log-likelihood estimator, Journal of Statistical Planning and Inference, Vol. 116, No. 1, pp. 149-162, 2003.
• Takafumi Kanamori, Noboru Murata. Boosting and its robustness, Journal of IEICE, Vol. 86, No. 10, pp. 769-772, 2003.
• Yoshua Bengio, Ichiro Takeuchi, Takafumi Kanamori. Robust Regression with Asymmetric Heavy-Tail Noise Distributions, Neural Computation, Vol. 14, No. 10, pp. 2469-2496, 2002.
• Takafumi KANAMORI. The Challenge of Non-Linear Regression on Large Datasets with Asymmetric Heavy Tails, Proceedings of the Joint Statistical Meeting, 2002.
• Takafumi KANAMORI. Statistical Asymptotic Theory of Active Learning, Annals of the Institute of Statistical Mathematics, Vol. 54, No. 3, pp. 459-475, 2002.
• Takafumi Kanamori, Hidetoshi Shimodaira. An Active Learning Algorithm Using an Information Criterion for the Maximum Weighted Log-likelihood Estimator, Proceedings of the Institute of Statistical Mathematics, Vol. 48, No. 1, 2000.
• Takafumi KANAMORI. Active Learning Algorighm using Maximum Weighted Likelihood Estimator, Bulletin of the Computational Statistics of Japan, Vol. 11, No. 2, 1998.

著書

• 金森敬文. Pythonで学ぶ統計的機械学習, オーム社, Oct. 2018.
• 金森敬文. Rによる機械学習入門, オーム社, Nov. 2017.
• Sugiyama, M., Suzuki, T., Kanamori, T.. Density Ratio Estimation in Machine Learning, Cambridge University Press,Cambridge,UK,, page 344, 2012.
• Takafumi KANAMORI. A New Sequential Algorithm for Regression Problems by using Mixture Distribution, Artificial Neural Networks ICANN 2002, Artificial Neural Networks ICANN 2002, pp. 535-540, 2002.

国際会議発表 (査読有り)

• Hiroaki Sasaki, Takafumi Kanamori, Masashi Sugiyama. Estimating Density Ridges by Direct Estimation of Density-Derivative-Ratios, the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Apr. 2017.
• Sugiyama, M., Suzuki, T., Kanamori, T., Du Plessis, M.C., Liu, S., Takeuchi, I.. Density-difference estimation, Neural Information Processing Systems(NIPS2012), Advances in Neural Information Processing Systems 25, P.Bartlett,F.C.N.Pereira,C.J.Burges,L.Bottou,and K.Q.Weinberger, pp. 692-700, 2012.
• Sugiyama, M., Takeuchi, I., Kanamori, T., Suzuki, T., Hachiya, H., Okanohara, D.. Conditional density estimation via least-squares density ratio estimation., Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS2010), In Proceedings of Thirteenth Conference on Artificial Intelligence and Statistics (AISTATS2010),, Vol. 9, pp. 781-788, May 2010.
• Sugiyama, M., Hara, S., von Bünau, P., Suzuki, T., Kanamori, T., Kawanabe, M.. Direct density ratio estimation with dimensionality reduction., the 10th SIAM International Conference on Data Mining (SDM2010), Proceeding of the 10th SIAM International Conference on Data Mining(SDM2010), pp. 595-606, Apr. 2010.
• Kanamori, T., Suzuki, T., Sugiyama, M.. Theoretical analysis of density ratio estimation., 12th Meeting of Special Interest Group on Data Mining and Statistical Mathematics, In Proceedings of the Japanese Society for Artificial Intelligence,, pp. 65-77, Mar. 2010.
• Sugiyama, M., Takeuchi, I., Suzuki, T., Kanamori, T., Hachiya, H., Okanohara, D.. Conditional density estimation based on density ratio estimation., IPSJSIGMathematicalModelling and ProblemSolving,, IPSJSIGTechnicalReport,, Vol. 2009-MPS-76, no. 4, Dec. 2009.
• Sugiyama, M., Hara, S., von Bünau, P., Suzuki, T., Kanamori, T., Kawanabe, M.. Dimensionality reduction for density ratio estimation based on Pearson divergence maximization., 2009Workshop on Information-Based Induction Sciences(IBIS2009), Nov. 2009.
• Kanamori, T., Suzuki, T., Sugiyama, M.. Condition number analysis of density ratio estimation., The 2009Japanese Joint Statistical Meeting, p. 163, 2009.
• Kanamori, T., Suzuki, T., Sugiyama, M.. Condition number analysis of kernel-based density ratio estimation., Numerical Mathematics in Machine Learning NUMML2009, 2009.
• Kanamori, T., Hido, S., Sugiyama, M.. Learning and density ratio estimation under covariate shift., The 2008 Japanese Joint Statistical Meeting, The 2008 Japanese Joint Statistical Meeting, p. 196, 2008.
• Sugiyama, M., Kanamori, T., Suzuki, T., Hido, S., Sese, J., Takeuchi, I., Wang, L.. Direct importance estimation - A new versatile tool for statistical pattern recognition, Meeting on Image Recognition and Understanding 2008 (MIRU2008), Meeting on Image Recognition and Understanding 2008 (MIRU2008), pp. 29-36, 2008.
• Suzuki, T., Sugiyama, M., Sese, J., Kanamori, T.. A least-squares approach to mutual information estimation with application in variable selection, Workshop on New Challenges for Feature Selection in Data Mining and Knowledge Discovery 2008 (FSDM2008), In Proceedings of Workshop on New Challenges for Feature Selection in Data Mining and Knowledge Discovery 2008 (FSDM2008), 2008.

国際会議発表 (査読なし・不明)

• takafumi kanamori. Statistical inference with unnormalized models, International Symposium on Statistical Theory and Methodology for Large Complex Data, Nov. 2018.
• takafumi kanamori. Statistical inference with unnormalized models, International Symposium on Statistical Theory and Methodology for Large Complex Data, Nov. 2018.
• Takafumi Kanamori. Integrability of Weak Learner on Boosting, Information Geometry and its Applications, to appear, Vol. -, No. -, pp. -, Dec. 2005.
• Takafumi Kanamori, Ichiro Takeuchi. Estimators for Conditional Expectations under Asymmetric and Heteroscedastic Error Distribution, International Symposium on The Art of Statistics Metaware, Institute of Statistical Mathematics, Vol. 1, pp. 312-313, Mar. 2005.

国内会議発表 (査読なし・不明)

• Nguyen, T.D., Du Plessis, M.C., Kanamori, T., & Sugiyama, M.. Constrained least-squares density-difference estimation, IBISML2012-104, IEICE Technical Report, pp. 79-86, 2013.
• 金森 敬文, 鈴木 大慈, 杉山 将.. 密度比の推定による２標本検定., 2010年度統計関連学会連合大会,, 2010年度統計関連学会連合大会,, pp. .52, Feb. 2012.
• Akiko Takeda, Takafumi Kanamori. Conditional Value-at-Risk Approach to Robust Optimization and Applications to Statistical Learning under Distribution Perturbation, Workshpo on Information-Based Induction Sciences, Proceedings of Information-Based Induction Science, Vol. 8, pp. 111-116, Nov. 2005.
• 竹内一郎, 野村要, 金森敬文. 区間線型パス追跡法による条件付分位点パスの計算, Workshop on Information-Based Induction Sciences, Vol. 8, pp. 105-110, Nov. 2005.

その他の論文・著書など

• 金森敬文, 樺島祥介, 高安美佐子, 中野 張, 福田光浩, 三好直人, 山下 真, 渡邊澄夫. 東京工業大学情報理工学院数理・計算科学系―情報の未来を作り出す数理的アプローチを探究する―, オペレーションズ・リサーチ, Vol. 64, No. 1, pp. 31-32, Jan. 2019.
• Sugiyama, M., Suzuki, T., Kanamori, T.. Density ratio estimation: A comprehensive review., RIMS Kôkyûroku, Reserch Institute for Mathematical Sciences, no. xxxx, pp. xxx－xxx, Mar. 2010.

特許など

• 金森敬文, 前　佑樹. 演算装置および学習済みモデル. 特許. 公開. 国立大学法人東京工業大学, 株式会社デンソー. 2020/09/17. PCT/JP2020/035254. 2021/05/20. WO 2021/095361. 2021.
• 金森敬文, 前　佑樹　. 演算装置および学習済みモデル. 特許. 登録. 国立大学法人東京工業大学, 株式会社デンソー. 2020/09/17. 特願2021-555926. 特許第7386462号. 2023/11/16 2023.

学位論文

• Statistical Asymptotic Theory of Active Learning,  本文,  Doctor of Philosophy,  The Graduate University for Advanced Studies,  1999/--/--, 

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