alt 2016.10 at Tonogayato Garden 

Song Liu (柳松), Doctor of Engineering

Lecturer in Data Science and A.I. at Computer Science Department,
University of Bristol, UK,

Email: song.liu(a-t)bristol.ac.uk

Short Bio

I am a lecturer in University of Bristol. Before, I was a Project Assistant Professor in The Institute of Statistical Mathematics, Tokyo, Japan. I got my Doctor of Engineering degree from Tokyo Institute of Technology supervised by Prof. Masashi Sugiyama and was awarded The DC2 Fellowship from Japan Society for the Promotion of Science.

Recently Organized Events

Research Interests

  • High Dimensional Sparse Structure Learning.

Peer Reviewed Papers

Liu, S., Takeda, A., Suzuki, T., Fukumizu, K.
Trimmed Density Ratio Estimation
preprint, Conference on Neural Information Processing Systems (NIPS), 2017, To appear.

Noh, Y-K., Sugiyama, M., Liu, S., du Plessis, M.C., Park, F.C., and Lee, D. D.,
Bias Reduction and Metric Learning for Nearest−Neighbor Estimation of Kullback−Leibler Divergence
To appear in Neural Computation, 2017

Yamada, M., Liu, S., Kaski S.,
Interpreting Outliers: Localized Logistic Regression for Density Ratio Estimation
preprint

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.
Annals of Statistics 45 (2017), no. 3, 959–990., 2017

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

Short Bio

  • 2017.9 - present: Lecturer in Data Science and A.I., Department of Computer Science, University of Bristol.

  • 2015.4 - 2017.9: Project Assistant Professor at Fukumizu Lab, The 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

  • 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].