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:
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(at)bristol.ac.uk) before application. Various funding opportunities are available for domestic and international students.
I can also supervise PhD students at Alan Turing Institute
Research Interests
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, YK., 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 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
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].
