News
Research Interests
Articles
Liu, S., Takeda, A., Suzuki, T., Fukumizu, K.
Trimmed Density Ratio Estimation
preprint, Conference on Neural Information Processing Systems (NIPS), 2017, To appear.
Yamada, M., Liu, S., Kaski S.,
Interpreting Outliers: Localized Logistic Regression for Density Ratio Estimation
arxiv
Liu, S., Fukumizu, K., Suzuki, T.
Learning Sparse Structural Changes in Highdimensional Markov Networks: A Review on Methodologies and Theories
preprint, Behaviormetrika,44:265, 2017, (Invited Paper).
Liu, S., Suzuki, T., Sugiyama, M., Fukumizu, K.
Structure Learning of Partitioned Markov Networks
preprint, Proceedings of The 33rd International Conference on Machine Learning, pp. 439–448, 2016.
Liu, S., Suzuki, T., Relator R., Sese J., Sugiyama, M., Fukumizu, K.,
Support Consistency of Direct SparseChange Learning in Markov Networks
Presented at NIPS workshop on Transfer and Multitask learning: Theory Meets Practice
preprint , Proceedings of TwentyNinth AAAI Conference on Artificial Intelligence (AAAI2015)
, pp.27852791, 2015.
To appear in Annals of Statistics, 2016
Noh, Y. K., Sugiyama, M., Liu S., du Plessis, M. C., Park, F. C., Lee, D. D.,
Bias Reduction and Metric Learning for NearestNeighbor Estimation of KullbackLeibler Divergence
In Proceedings of Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS2014), volume 33, pages 669677, 2014 Reykjavik, Iceland, Apr. 2224, 2014.
Liu, S., Quinn, J. A., Gutmann, M. U., Suzuki, T., Sugiyama, M.,
Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation.,
Neural Computation, 26(6):11691197, 2014
software, pdf
Liu, S., Quinn, J. A., Gutmann, M. U., Sugiyama, M.,
Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation.,
In H. Blockeel, K. Kersting, S. Nijssen and F. Železný (Eds.), Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD2013) Part II, pp.596611, Prague, Czech Republic, Sep. 2327, 2013.
pdf
Sugiyama, M., Liu, S., du Plessis, M. C., Yamanaka, M., Yamada, M., Suzuki, T., & Kanamori, T.
Direct divergence approximation between probability distributions and its applications in machine learning.
Journal of Computing Science and Engineering, vol.7, no.2, pp. 99111, 2013.
pdf
Liu, S., Yamada, M., Collier, N., Sugiyama M.,
Changepoint detection in timeseries data by relative densityratio estimation,
Neural Networks, vol. 43, July 2013, pp. 7283, ISSN 08936080.
pdf, software, arxiv entry
Liu, S., Yamada, M., Collier, N., & Sugiyama, M.
Changepoint detection in timeseries data by relative densityratio estimation.
In G. Gimel'farb, E. Hancock, A. Imiya, A. Kuijper, M. Kudo, S. Omachi, T. Windeatt, and K Yamada (Eds.), Structural, Syntactic, and Statistical Pattern Recognition, Lecture Notes in Computer Science, vol.7626, pp.363372, Berlin, Springer, 2012.
(Presented at 9th International Workshop on Statistical Techniques in Pattern Recognition (SPR2012), Hiroshima, Japan, Nov. 79, 2012)
pdf, slides
Sugiyama, M., Suzuki, T., Kanamori, T., du Plessis, M. C., Liu, S., & Takeuchi, I.
Densitydifference estimation.
In P. Bartlett, F. C. N. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 25, pp.692700, 2012.
(Presented at Neural Information Processing Systems (NIPS2012), Lake Tahoe, Nevada, USA, Dec. 36, 2012)
pdf
Talks
2016.3.25: Talk at Probabilistic Graphical Model Workshop, ISM, on Graphical Models and Density Ratio.
2016.2.15: Talk at The Gatsby Computational Neuroscience Unit, UCL, on my recent researches.
2016.2.10: Talk at Computer Science Department, University of Bristol, on my recent researches.
2015.12.12: Talk at Transfer and MultiTask Learning Workshop at NIPS2015, on Transfer Learning.
2015.6.23: Talk at Okinawa Institute of Science and Technology, on Transfer Learning.
2015.1.30: Talk at University of Pennsylvania, Wharton School, on Learning Changes from Graphical Models.
2014.12.25: Talk at Institute of Statistical Mathematics, Japan on Learning Changes from Graphical Models.
2014.8.18: Talk at Dept. Computer Science, Soochow University on Change Detection.
2014.3.18: Talk at Sheffield Institute of Translational Neuroscience, on Change Detection.
2014.2.3: Talk at National Institute of Informatics, on Change Detection.
2013.11.22: Talk at National Institute of Informatics, on Timeseries Change Detection.
2013.9.26: Talk at ECML/PKDD 2013, on Structural Change Detection. Slides
2013.7.18: Talk at IBISML 2013, Waseda University, Tokyo, on Learning Changes from Graphical Models. Slides
2013.7.2: Talk at IBM Research Tokyo, Toyosu.
Short Bio
Technical Report
Liu S., Flach P, Cristianini N.
Generic Multiplicative Methods for Implementing Machine Learning Algorithms on MapReduce.
arXiv:1111.2111 [cs.DS].
