Journal papers (Refereed):
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Taiji Suzuki:
Fast Learning Rate of Non-Sparse Multiple Kernel Learning and Optimal Regularization Strategies.
Electronic Journal of Statistics, Volume 12, Number 2 (2018), 2141--2192. doi:10.1214/18-EJS1399.
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Yuichi Mori and Taiji Suzuki:
Generalized ridge estimator and model selection criteria in multivariate linear regression.
Journal of Multivariate Analysis, volume 165, pages 243--261, May 2018.
arXiv:1603.09458.
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Song Liu, Taiji Suzuki, Relator Raissa, Jun Sese, Masashi Sugiyama, and Kenji Fukumizu:
Support Consistency of Direct Sparse-Change Learning in Markov Networks.
The Annals of Statistics, vol. 45, no. 3, 959–990, 2017. DOI: 10.1214/16-AOS1470.
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Song Liu, Kenji Fukumizu and Taiji Suzuki:
Learning Sparse Structural Changes in High-dimensional Markov Networks: A Review on Methodologies and Theories.
Behaviormetrika. 44(1):265–286, 2017. DOI: 10.1007/s41237-017-0014-z.
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Yoshito Hirata, Kai Morino, Taiji Suzuki, Qian Guo,
Hiroshi Fukuhara, and Kazuyuki Aihara:
System Identification and Parameter Estimation in
Mathematical Medicine: Examples Demonstrated for Prostate Cancer.
Quantitative Biology, 2016, 4(1): 13--19. DOI: 10.1007/s40484-016-0059-0.
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Taiji Suzuki:
Stochastic Alternating Direction Method of
Multipliers for Structured Regularization.
Journal of Japan Society of Computational Statistics, 28(2015), 105--124
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Taiji Suzuki, and Kazuyuki Aihara:
Nonlinear System Identification for Prostate Cancer and Optimality of Intermittent Androgen Suppression Therapy.
Mathematical Biosciences, vol. 245, issue 1, pp. 40--48, 2013.
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Taiji Suzuki, and Masashi Sugiyama:
Fast learning rate of multiple kernel learning: trade-off between sparsity and smoothness.
The Annals of Statistics, vol. 41, number 3, pp. 1381-1405, 2013.
(arXiv version, arXiv:1203.0565)
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Taiji Suzuki:
Improvement of Multiple Kernel Learning using Adaptively Weighted Regularization.
JSIAM Letters, vol. 5, pp. 49--52, 2013.
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Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, M. C. du Plessis, Song Liu, Ichiro Takeuchi:
Density Difference Estimation.
Neural Computation, 25(10): 2734--2775, 2013.
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Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama,
Relative Density-Ratio Estimation for Robust Distribution Comparison.
Neural Computation, vol. 25, number 5, pp. 1324--1370, 2013.
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Takafumi Kanamori, Taiji Suzuki, and Masashi Sugiyama:
Computational complexity of kernel-based density-ratio estimation: A condition number analysis.
Machine Learning, vol. 90, pp. 431-460, 2013.
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Taiji Suzuki, and Masashi Sugiyama:
Sufficient dimension reduction via squared-loss mutual information estimation.
Neural Computation, vol. 25, pp. 725-758, 2013.
(software (matlab))
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Takafumi Kanamori, Taiji Suzuki, and Masashi Sugiyama:
Statistical analysis of kernel-based least-squares density-ratio estimation.
Machine Learning, vol. 86, Issue 3, pp. 335-367, 2012.
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Takafumi Kanamori, Taiji Suzuki, and Masashi Sugiyama:
f-divergence estimation and two-sample homogeneity test under semiparametric density-ratio models.
IEEE Transactions on Information Theory, Vol. 58, Issue 2, pp. 708-720, 2012.
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Masashi Sugiyama, Taiji Suzuki, and Takafumi Kanamori:
Density ratio matching under the Bregman divergence: A unified framework of density ratio estimation.
Annals of the Institute of Statistical Mathematics, vol. 11, pp. 1--36, 2011.
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Taiji Suzuki and Ryota Tomioka:
SpicyMKL: A Fast Algorithm for Multiple Kernel Learning with Thousands of Kernels.
Machine Learning, vol. 85, issue 1, pp. 77--108, 2011.
(arXiv:0909.5026,
METR,
slide (pptm, pdf) in one-day workshop at ISM, software)
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Masashi Sugiyama, Taiji Suzuki, Yuta Itoh, Takafumi Kanamori, and Manabu Kimura:
Least-Squares Two-Sample Test.
Neural Networks, vol.24, no.7, pp.735--751, 2011.
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Ryota Tomioka, Taiji Suzuki, and Masashi Sugiyama:
Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparse Learning.
Journal of Machine Learning Research, 12(May):1537--1586, 2011.
(arXiv:0911.4046)
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Masashi Sugiyama, Makoto Yamada, Paul von Bunau, Taiji Suzuki, Takafumi Kanamori, and Motoaki Kawanabe:
Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search.
Neural Networks, vol.24, no.2, pp.183-198, 2011.
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Taiji Suzuki, Nicholas Bruchovsky, and Kazuyuki Aihara:
Piecewise Affine Systems Modelling for Optimizing Hormonal Therapy of Prostate Cancer.
Philosophical Transactions A of the Royal Society, 368 (2010), 5045--5059.
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Taiji Suzuki, and Masashi Sugiyama:
Least-squares Independent Component Analysis.
Neural Computation, 23(1) (2011), 284--301.
(software)
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Masashi Sugiyama, and Taiji Suzuki:
Least-squares independence test.
IEICE Transactions on Information and Systems, vol.E94-D, no.6, pp.1333-1336, 2011.
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Takafumi Kanamori, Taiji Suzuki, and Masashi Sugiyama:
Theoretical analysis of density ratio estimation.
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol.E93-A, no.4, pp.787--798, 2010.
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Masashi Sugiyama, Ichiro Takeuchi, Takafumi Kanamori, Taiji Suzuki, Hirotaka Hachiya, and Daisuke Okanohara:
Least-squares conditional density estimation.
IEICE Transactions on Information and Systems, vol.E93-D, no.3, pp.583-594, 2010.
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Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Shohei Hido, Jun Sese, Ichiro Takeuchi, and Liwei Wang:
A density-ratio framework for statistical data processing. IPSJ Transactions on Computer Vision and Applications, 1 (2009), 183--208.
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Taiji Suzuki, Masashi Sugiyama, Takafumi Kanamori, and Jun Sese:
Mutual information estimation reveals global associations
between stimuli and biological processes.
BMC Bioinformatics, 10(Suppl 1):S52, 2009.
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Masashi Sugiyama, Taiji Suzuki, Shinichi Nakajima, Hisashi Kashima, Paul von Bunau, and Motoaki Kawanabe:
Direct importance estimation for covariate shift adaptation.
Annals of the Institute of Statistical Mathematics.
60(4) (2008), 699--746.
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Taiji Suzuki, and Fumiyasu Komaki:
On prior selection and covariate shift of $\beta$-Bayesian
prediction under $\alpha$-divergence risk.
Communications in Statistics --- Theory and Methods, 39(8) (2010), 1655--1673.
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Akimichi Takemura, and Taiji Suzuki:
Game-Theoretic Derivation of Discrete Distributions and Discrete Pricing Formulas.
Journal of Japan Statistical Society, 37 (1) (2006), 87--104.
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Taiji Suzuki, Satoshi Aoki, and Kazuo Murota:
Use of primal-dual technique in the network algorithm for two-waycontingency tables.
Japan Journal of Industrial and Applied Mathematics, 22 (1) (2005), 133--145.
(Errata)
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