Song Liu (柳松), University of Bristol.

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,

Turing Fellow at Alan Turing Institute.

Email: song.liu(a-t)

UoB student book a meeting with me via outlook

PhD Candidates Wanted

I am now looking for good PhD candidates to work together on statistical machine learning research. In general, the student is expected to utilize statistical/mathematical tools to design and understand machine learning algorithms. My current interests are:

  • Adversarial learning: analyze the current machine learning algorithm from an adversarial perspective. Understand how potential attacks may happen and investigate possible defence mechanisms.

I also welcome students who want to work on other domains of machine learning, such as transfer learning, probabilistic graphical models and topological data analysis. Potential candidates are encouraged to contact me via his email (song.liu(a-t) before application. Various funding opportunities are available for domestic and international students.

I can also supervise PhD students at Alan Turing Institute

Research Interests

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

  • Sparse Learning, Density Ratio Estimation

  • Change-point Detection, Anomaly Detection

Selected Publications

Kim, B., Liu, S., Kolar, M.,
Two-sample inference for high-dimensional Markov networks
Preprints, Under review.

Wu, X. Z., Liu, S., Zhou Z.H.
Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin
PDF, Proceedings of the 36 th International Conference on Machine Learning (ICML 2019), PMLR 97, 2019.

Liu, S., Jitkrittum, W., Kanamori, T., Chen, Y.
Fisher Efficient Inference of Intractable Models
preprint, Presented at Workshop on Tractable Probabilistic Models (TPM 2018).

Liu, S., Takeda, A., Suzuki, T., Fukumizu, K.
Trimmed Density Ratio Estimation
PDF, Advances in Neural Information Processing Systems 30 (NIPS 2017), 2017.

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

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.

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.

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)

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, 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].