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Machine Learning
Machine learning is a branch of computer science that develops algorithms to help us make sense of data. Increasingly, these algorithms are finding applications in systems that need to make predictions based on uncertain or incomplete information.
Our research is both theoretical and applied. We aim to understand the theoretical foundations of how machines learn, their guarantees and limitations, and the relationship between different approaches to learning. Our work is applied to data-rich areas such as social networking, understanding and interpreting images and natural language, biomedical applications, and economic modelling.
We conduct independent research as well as collaborative investigations with other academic and industry groups from Australia and overseas. Highlights of our work include: understanding images and analysing large amounts of social media content to reveal laws of community behaviour; developing theory of learning objectives and their relationship to optimisation algorithms; and discovering new methods to understand complex patterns in static and time series data.
We are advancing machine learning technologies to help people and organisations make better decisions.
Explore our available student research projects below and if you’d like to discuss opportunities for collaboration or funding, please email us.
Student research projects
Research projects
Academic staff
Professor Amanda Barnard »
Senior Professor of Computational Science, Cluster Lead, Computational Science, Deputy Director, School of Computing
Hongdong Li »
Professor, Computer Vision |Machine Learning |Robotics |AI , Chief Investigator for ARC CoE ACRV, IEEE T-PAMI Editor
Associate Professor Hanna Suominen »
Associate Professor and Team Leader in Machine Learning, Analytics and Decision Sciences Program, Data61/CSIRO
Professor Kerry Taylor »
Professor (Data Science), Convenor, Postgraduate Programs in Applied Data Analytics, Convenor, Undergraduate Programs in Applied Data Analytics
Professor Graham Williams »
Professor, Chief Scientist, Software Innovation Institute, Cluster Lead, Data Science & Analytics
Student
Affiliates
Visitors
Technical staff
Collaborator
2010
Book Chapters
- Buntine, W., (2010). Bayesian Methods. In Claude Sammut & Geoffrey I.Webb (eds.), Encyclopedia of Machine Learning, Springer, ISBN: 9780387307688.
- McAuley, J., Caetano, T., Buntine, W., (2010). Graphical Models. In Claude Sammut & Geoffrey I.Webb (eds.), Encyclopedia of Machine Learning, Springer, ISBN: 9780387307688.
- Quadrianto, N., Buntine, W., (2010). Linear Discriminant. In Claude Sammut & Geoffrey I.Webb (eds.), Encyclopedia of Machine Learning, Springer, ISBN: 9780387307688.
- Quadrianto, N., Buntine, W., (2010). Linear Regression. In Claude Sammut & Geoffrey I.Webb (eds.), Encyclopedia of Machine Learning, Springer, ISBN: 9780387307688.
- Quadrianto, N., Buntine, W., (2010). Regression. In Claude Sammut & Geoffrey I.Webb (eds.), Encyclopedia of Machine Learning, Springer, ISBN: 9780387307688.
- Reid, M., (2010). Generalization Bounds. In Claude Sammut & Geoffrey I.Webb (eds.), Encyclopedia of Machine Learning, Springer, ISBN: 9780387307688.
Journal Articles
- Bonilla, E., Guo, S., Sanner, S., (2010). Gaussian Process Preference Elicitation. Advances in Neural Information Processing Systems, 23:262–270.
- Du, L., Buntine, W., Jin, H., (2010). A segmented topic model based on the two-parameter Poisson-Dirichlet process. Machine Learning, 81:5–19.
- lein, G., Andronick, J., Elphinstone, K., Heiser, G., Cock, D., Derrin, P., Elkaduwe, D., Engelhardt, K., Kolanski, R., Norrish, M., Sewell, T., Tuch, H., Winwood, S., (2010). SeL4: Formal verification of an operating-system kernel. Communications of the Association for Computing Machinery, 53(6):107–115.
- Reid, M., Williamson, R., (2010). Composite binary losses. Journal of Machine Learning Research, 11:2387–2422.
- Reid, M., Williamson, R., (2010). Convexity of proper composite binary losses. , pp. 637–644.
Conference Papers
- Du, L., Buntine, W., Jin, H., (2010). A segmented topic model based on the two-parameter Poisson-Dirichlet process. Machine Learning, 81:5–19.
- Barthwal, A., Norrish, M., (2010). A Formalisation of the Normal Forms of Context-Free Grammars in HOL4. In Anuj Dawar (ed.), Workshop on Logic, Language, Information and Computation 2010, pp. 15, Brasília Brazil.
- Barthwal, A., Norrish, M., (2010). Mechanisation of PDA and Grammar Equivalence for Context-Free Languages. In Anuj Dawar (ed.), Workshop on Logic, Language, Information and Computation 2010, pp. 10, Brasília Brazil.
- Bonilla, E., Dubach, C., Jones, T., O'Boyle, M., (2010). A predictive model for dynamic microarchitectural adaptivity control. In Annual IEEE/ACM International Conference on Microarchitecture 2010, pp. 12, Atlanta USA
- Bouguettaya, A., Chen, S., Li, L., Liu, D., Liu, Q., Nepal, S., Sherchan, W., Wu, J., Zhou, X., (2010).Managing Web Services: An Application in Bioinformatics. In International Conference on Service Oriented Computing (ICSOC 2010), pp. 2, San Francisco USA.
- Buntine, W., Du, L., Nurmi, P., (2010). Bayesian Networks on Dirichlet Distributed Vectors. In Petri Myllymäki, (eds.), European Workshop on Probabilistic Graphical Models, pp. 8, Helsinki Finland
- Caetano, T., McAuley, J., (2010). exploiting Data-Independence for fast Belief-Propargation. In Johannes Fürnkranz (ed.), International Conference on Machine Learning (ICML 2010), Haifa Israel.
- Du, L., Buntine, W., Jin, H., (2010). Sequential Latent Dirichlet Allocation: Discover Underlying Topic Structures within a Document. In IEEE International Conference on Data Mining 2010, pp. 10, Sydney Australia.
- Guo, S., Sanner, S., (2010). Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries. In Liqing Zhang (ed.), International Symposium on Neural Networks (ISNN 2010), pp. 8, Shanghai China.
- Guo, S., Sanner, S., (2010). Probabilistic Latent Maximal Marginal Relevance. In Annual ACM SIGIR Conference 2010, pp. 2, Geneva Switzerland.
- Guo, S., Sanner, S., (2010). Real-time multiattribute Bayesian preference elicitation with pairwise comparison queries. In Yee Whye Teh, Mike Titterington (eds.), International Conference on Artificial Intelligence and Statistics (AISTATS 2010), pp. 289–296, Sardinia Italy.
- Kumar, R., Norrish, M., (2010). (Nominal) Unification by Recursive Descent with Triangular Substitutions. In International Conference on Interactive Theorem Proving (ITP 2010), pp. 16, Edinburgh Scotland
- McAuley, J., Caetano, T., (2010). Exploiting within-Clique factorizations in junction-tree algorithms. In Yee Whye Teh, Mike Titterington (eds.), International Conference on Artificial Intelligence and Statistics (AISTATS 2010), pp. 525–532, Sardinia Italy.
- McAuley, J., de Campos, T., Caetano, T., (2010). Unified Graph Matching in Euclidean Spaces. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010), San Francisco USA.
- Petterson, J., Smola, A., Caetano, T., Buntine, W., Narayanamurthy, S., (2010). Word Features for Latent Dirichlet Allocation. In International Conference on Neural Information Processing (ICONIP 2010), pp. 10, Sydney Australia.
- Quadrianto, N., Smola, A., Caetano, T., Vishwanatha, S.V.N., Petterson, J., (2010). Multitask Learning without Label Correspondences. In International Conference on Neural Information Processing (ICONIP 2010), pp. 9, Sydney Australia.
- Tran, K.-N., Jin, H., (2010). Detecting Network Anomalies in Mixed-Attribute Data Sets. In Third International Conference on Knowledge Discovery and Data Mining, pp. 383–386, Phuket, Thailand
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