@book{CTT100660187,
  author = {Sugiyama, M. and Suzuki, T. and Kanamori, T.},
  title = {Density Ratio Estimation in Machine Learning},
  publisher = {},
  year = 2012,
}

@article{CTT100660176,
  author = {Yamada, M. and Suzuki, T. and Kanamori, T. and Hachiya, H. and Sugiyama, M.},
  title = {Relatiive density-ratio estimationfor robust distribution comparision},
  journal = {Neural Computation},
  year = 2013,
}

@article{CTT100660181,
  author = {Kanamori and Suzuki, T. and Sugiyama, M.},
  title = {Computational complexity of kernel-based density-ratio estimation:A condition number analysis},
  journal = {Machine Learning},
  year = 2013,
}

@article{CTT100660151,
  author = {Suzuki, T. and Sugiyama, M.},
  title = {Fast learning rate of multiple kernel learning:Trade-off between sparsity and smoothness.},
  journal = {The Annals of Statistics},
  year = 2013,
}

@article{CTT100660183,
  author = {Suzuki, T. and & Sugiyama, M.},
  title = {Sufficient dimension reduction via squared-loss mutual information  estimation},
  journal = {Neural Computation},
  year = 2013,
}

@article{CTT100660172,
  author = {Sugiyama, M. and Liu, S. and Du Plessis, M.C. and Yamanaka, M. and Yamada, M. and Suzuki, T. and & Kanamori, T.},
  title = {Direct divergence approximationbetween probability distributions and its applications inmachine learning},
  journal = {Journal of Computing Science and Engineering},
  year = 2013,
}

@article{CTT100660277,
  author = {Kanamori, T. and Suzuki, T. and Sugiyama, M.},
  title = {Statistical analisis of kernel-based least-squares density-ratio estimation},
  journal = {Machine Learning},
  year = 2012,
}

@article{CTT100660279,
  author = {Kanamori, T. and Suzuki, T. and Sugiyama, M.},
  title = {F-divergence estimation and two-sample homogeneity test under semiparametric density-ratio models},
  journal = {IEEE Transactions on Information Theory},
  year = 2012,
}

@article{CTT100660273,
  author = {Sugiyama, M. and Suzuki, T. and Kanamori, T},
  title = {Density ratio matching under the Bregman divergence:A unified framework of density ratio estimation},
  journal = {Annals of the Institlute of Statistical Mathematics},
  year = 2012,
}

@inproceedings{CTT100660235,
  author = {Sugiyama, M. and Suzuki, T. and Kanamori, T. and Du Plessis, M.C. and Liu, S. and Takeuchi, I.},
  title = {Density-difference estimation},
  booktitle = {Advances in Neural Information Processing Systems 25},
  year = 2012,
}