論文

• 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.
• Suzuki, T., Sugiyama, M.. Fast learning rate of multiple kernel learning:Trade-off between sparsity and smoothness., The Annals of Statistics, vol. 41, no. 3, pp. 1381-1405, 2013.
• Suzuki, T., & Sugiyama, M.. Sufficient dimension reduction via squared-loss mutual information estimation, Neural Computation, vol. 25, no. 3, pp. 725-758, 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.
• 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.
• 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.

著書

• Sugiyama, M., Suzuki, T., Kanamori, T.. Density Ratio Estimation in Machine Learning, Cambridge University Press,Cambridge,UK,, page 344, 2012.

国際会議発表 (査読有り)

• 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.

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