photo
鈴木大慈        TITECHLOGO   PRESTO
Associate Professor

Department of Mathematical Informatics
Graduate School of Information Science and Technology
The University of Tokyo

Center for Advanced Integrated Intelligence Research, RIKEN, Tokyo, Japan

Room: Room No. 352, Faculty of Engineering Building 6 (map)
Postal Address: Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, JAPAN
Phone: +81-3-5841-6921
E-mail: e-mail

Topic

  • I am looking for Postdoc for machine learning research.
  • I have moved to University of Tokyo (Apr/2017).
  • MLSS 2015 "Stochastic optimization" slides: slide1, slide2, slide3

Research Interests

I am interested in Statistics and Machine Learning, especially the following research topics.
  • Statistical learning theory
    • Sparse estimation in high dimensional data
    • Nonparametric convergence analysis
  • Convex optimization
    • Online-Stochastic optimization
    • Discrete convex analysis
  • Information geometry
    • Prior selection
    • Objective Bayes
  • Control theory
    • Hybrid systems

CV

Publications and Presentations

New:
  • Tomoya Murata, Taiji Suzuki: Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation. NIPS2018, accepted. arXiv:1809.01765.
  • Taiji Suzuki, Hiroshi Abe, Tomoya Murata, Shingo Horiuchi, Kotaro Ito, Tokuma Wachi, So Hirai, Masatoshi Yukishima, Tomoaki Nishimura: Spectral-Pruning: Compressing deep neural network via spectral analysis. arXiv:1808.08558.
  • Atsushi Nitanda, Taiji Suzuki: Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors. arXiv:1806.05438.
  • 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.
  • Atsushi Nitanda and Taiji Suzuki: Stochastic Particle Gradient Descent for Infinite Ensembles. arXiv:1712.05438.
  • Ryota Tomioka and Taiji Suzuki: Spectral norm of random tensors. arXiv:1407.1870.
Journal papers (Refereed):
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Taiji Suzuki: Stochastic Alternating Direction Method of Multipliers for Structured Regularization. Journal of Japan Society of Computational Statistics, 28(2015), 105--124
  • 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.
  • 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)
  • Taiji Suzuki: Improvement of Multiple Kernel Learning using Adaptively Weighted Regularization. JSIAM Letters, vol. 5, pp. 49--52, 2013.
  • Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, M. C. du Plessis, Song Liu, Ichiro Takeuchi: Density Difference Estimation. Neural Computation, 25(10): 2734--2775, 2013.
  • 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.
  • 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.
  • Taiji Suzuki, and Masashi Sugiyama: Sufficient dimension reduction via squared-loss mutual information estimation. Neural Computation, vol. 25, pp. 725-758, 2013. (software (matlab))
  • 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.
  • 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.
  • 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.
  • 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)
  • 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.
  • 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)
  • 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.
  • 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.
  • Taiji Suzuki, and Masashi Sugiyama: Least-squares Independent Component Analysis. Neural Computation, 23(1) (2011), 284--301. (software)
  • Masashi Sugiyama, and Taiji Suzuki: Least-squares independence test. IEICE Transactions on Information and Systems, vol.E94-D, no.6, pp.1333-1336, 2011.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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)
International Conference papers (Refereed):
Book:
  • Masashi Sugiyama, Taiji Suzuki, & Takafumi Kanamori: Density Ratio Estimation in Machine Learning. Cambridge University Press, 2012.
Invited Talk:
Technical Report:
  • Taiji Suzuki, Ryota Tomioka, Masashi Sugiyama: Fast Convergence Rate of Multiple Kernel Learning with Elastic-net Regularization. arXiv:1103.0431. (slide in Japanese)
  • Taiji Suzuki, Ryota Tomioka, and Masashi Sugiyama: Sharp Convergence Rate and Support Consistency of Multiple Kernel Learning with Sparse and Dense Regularization. arXiv:1103.5201.
  • Taiji Suzuki: Fast Learning Rate of lp-MKL and its Minimax Optimality. arXiv:1103.5202.
  • Taiji Suzuki, and Ryota Tomioka: SpicyMKL. arXiv:0909.5026, METR. (slide (pptm, pdf) in one-day workshop at ISM, software)
  • Taiji Suzuki, Satoshi Aoki and Kazuo Murota: Use of primal-dual technique in the network algorithm for two-waycontingency tables. METR 2004-28, Department of Mathematical Informatics, University of Tokyo, May 2004. (pdf) (Errata)
  • Akimichi Takemura and Taiji Suzuki: Game Theoretic Derivation of Discrete Distributions and Discrete Pricing Formulas. METR2005-25, Department of Mathematical Informatics, University of Tokyo, September 2005. (pdf)
Article in Book:
  • Ryota Tomioka, Taiji Suzuki, & Masashi Sugiyama: Augmented Lagrangian methods for learning, selecting, and combining features. In S. Sra, S. Nowozin, and S. J. Wright (Eds.), Optimization for Machine Learning, MIT Press, Cambridge, MA, USA, 2011.
  • Masashi Sugiyama, Taiji Suzuki, & Takafumi Kanamori: Density ratio estimation: A comprehensive review. In Statistical Experiment and Its Related Topics, Research Institute for Mathematical Sciences Kokyuroku, no.1703, pp.10-31, 2010. (Presented at Research Institute for Mathematical Sciences Workshop on Statistical Experiment and Its Related Topics, Kyoto, Japan, Mar. 8-10, 2010)
  • Ryota Tomioka, Taiji Suzuki, & Masashi Sugiyama: Optimization algorithms for sparse regularization and multiple kernel learning and their applications to image recognition. Image Lab, vol.21, no.4, pp.5-11, 2010.
Symposium:
Award:
  • Taiji Suzuki: The Japan Society for Industrial and Applied Mathematics, Best paper award 2016. Improvement of Multiple Kernel Learning using Adaptively Weighted Regularization.
  • Taiji Suzuki: IBISML (Information-Based Induction Sciences and Machine Learning), Best paper award 2012 (2012年度IBISML研究会賞). Dual Averaging and Proximal Gradient Descent for Online Alternating Direction Multiplier Method.
  • Taiji Suzuki: 情報理工学系研究科長賞 東京大学大学院情報理工学系研究科,2009年.
  • Taiji Suzuki: 情報理工学系研究科長賞 東京大学大学院情報理工学系研究科,2006年.
  • MIRU優秀論文賞, Meeting on Image Recognition and Understanding 2008 (MIRU2008), 2008年 "Direct Importance Estimation - A New Versatile Tool for Statistical Pattern Recognition" Masashi Sugiyama (Tokyo Institute of Technology), Takafumi Kanamori (Nagoya University), Taiji Suzuki (University of Tokyo), Shohei Hido (IBM Research), Jun Sese (Ochanomizu University), Ichiro Takeuchi (Mie University), and Liwei Wang (Peking University).
Domestic Conference and Meeting:
    list (in Japanese).

Miscellaneous materials