Song Liu (柳松), Fukumizu Lab.

alt 2016.10 at Tonogayato Garden 

Song Liu (柳松), Doctor of Engineering

Project Assistant Professor at Fukumizu Lab,
The Institute of Statistical Mathematics, Tokyo, Japan,

Email: liu(a-t)ism.ac.jp
Facebook: http://facebook.com/song.andy.liu

News

We have just organized a workshop on Proabilistic Graphical Models! Slides are online! Check out our website!

See last year (2016)'s workshop.

Research Interests

  • Probabilistic Graphical Models, Markov Random Field(Markov Network)

  • Sparse Learning, Density Ratio Estimation

  • Change-point Detection, Anomaly Detection

Articles

Several 2017 preprints are not shown here, please contact me if you are interested.

Liu, S., Fukumizu, K., Suzuki, T.
Learning Sparse Structural Changes in High-dimensional 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 Sparse-Change Learning in Markov Networks
Presented at NIPS workshop on Transfer and Multi-task learning: Theory Meets Practice
preprint , Proceedings of Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI2015)
, pp.2785-2791, 2015.
To appear in Annals of Statistics, 2016

Liu, S., Fukumizu, K.,
Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective
Proceedings of 2016 SIAM International Conference on Data Mining (SDM2016),pp.747-755
Presented at NIPS workshop on Transfer and Multi-Task Learning: Trends and New Perspectives.
preprint, 2015.

Yacine, C., Liu, S., Sugiyama M., Hideaki I.,
Statistical Outlier Detection for Diagnosis of Cyber Attacks in Power State Estimation
2016 IEEE Power and Energy Society General Meeting (PESGM), pp. 1-5, 2016

Noh, Y. -K., Sugiyama, M., Liu S., du Plessis, M. C., Park, F. C., Lee, D. D.,
Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence
In Proceedings of Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS2014), volume 33, pages 669-677, 2014 Reykjavik, Iceland, Apr. 22-24, 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):1169-1197, 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.596-611, Prague, Czech Republic, Sep. 23-27, 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. 99-111, 2013.
pdf

Liu, S., Yamada, M., Collier, N., Sugiyama M.,
Change-point detection in time-series data by relative density-ratio estimation,
Neural Networks, vol. 43, July 2013, pp. 72-83, ISSN 0893-6080.
pdf, software, arxiv entry

Liu, S., Yamada, M., Collier, N., & Sugiyama, M.
Change-point detection in time-series data by relative density-ratio 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.363-372, Berlin, Springer, 2012.
(Presented at 9th International Workshop on Statistical Techniques in Pattern Recognition (SPR2012), Hiroshima, Japan, Nov. 7-9, 2012)
pdf, slides

Sugiyama, M., Suzuki, T., Kanamori, T., du Plessis, M. C., Liu, S., & Takeuchi, I.
Density-difference 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.692-700, 2012.
(Presented at Neural Information Processing Systems (NIPS2012), Lake Tahoe, Nevada, USA, Dec. 3-6, 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 Multi-Task 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 Time-series 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

  • 2015.4 - present: Project Assistant Professor at Fukumizu Lab, Institute of Statistical Mathematics, Tokyo.

  • 2014.4 - 2015.3: Postdoc at Sugiyama Lab, Tokyo Institute of Technology.

  • 2014.3: Graduated from Tokyo Institute of Technology as Doctor of Engineering (supervised by Masashi Sugiyama).

  • 2010.11: Graduated from University of Bristol, UK, with MSc Degree (Distinction)

  • 2009.6: Graduated from Soochow University, China, with BEng degree (GPA: 3.43)

  • Born on 1987/10/8, Nanjing, China.

Technical Report

Liu S., Flach P, Cristianini N.
Generic Multiplicative Methods for Implementing Machine Learning Algorithms on MapReduce.
arXiv:1111.2111 [cs.DS].