The machine learning group in Cambridge develops new machine learning methods, taking them from theory to generic computer implementations. It also works on the application of these new methods, as well as more established ones, in new domains.
- Infer.NET – our main focus, Infer.NET is a framework for automatically applying probabilistic inference to a large variety of problems. Infer.NET has required the development of new, modular machine learning algorithms, along with new ways of architecting inference software that can deliver efficient, customised code tailored to solve particular problems. This work has also led to developments in probabilistic programming.
- Machine learning theory – much of our theoretical research relates to deterministic approximate algorithms such as Expectation Propagation and Variational Message Passing. More recently, our theoretical work has focused on solutions needed for the Infer.NET framework, such as new scheduling algorithms and gates.
- Bioinformatics applications – in collaboration with the Wellcome Trust Sanger Institute we are developing new methods to understand the function of our genes and investigate how genetic variation can give rise to common human diseases.
- Healthcare applications – electronic healthcare records contain information about the nature of disease and the effectiveness of treatments that could have enormous benefits for medicine and public health. We are developing methods to help unlock this information from the wealth of varied and imperfect healthcare data.
- Other machine learning applications – we work with many of the other groups from MSR Cambridge and beyond to build machine learning solutions to problems in machine vision, informational retrieval, human-computer interaction, game theory, software development and many other domains.
Machine Learning and Perception
> Machine Learning at MSR Cambridge
- S. M. Eslami, N. Heess, and J. Winn, The Shape Boltzmann Machine: a Strong Model of Object Shape, in Proc. Conf. Computer Vision and Pattern Recognition (to appear), July 2012
- John Winn, Causality with Gates, in Proceedings Artificial Intelligence and Statistics, The Society for Artificial Intelligence and Statistics, April 2012
- Oliver Stegle, Leopold Parts, Matias Piipari, John Winn, and Richard Durbin, Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses, in Nature Protocols, vol. 7, pp. 500-507, Nature Publishing Group, February 2012
- Yoram Bachrach, Tom Minka, John Guiver, and Thore Graepel, How To Grade a Test Without Knowing the Answers - A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing, in ICML , ICML, 2012
- Reshef Meir, Moshe Tennenholtz, Yoram Bachrach, and Peter Key, Congestion Games with Agent Failures, in AAAI 2012, Association for the Advancement of Artificial Intelligence, 2012
- Xi Alice Gao, Yoram Bachrach, Peter Key, and Thore Graepel, Quality Expectation-Variance Tradeoffs in Crowdsourcing Contests, in AAAI 2012, Association for the Advancement of Artificial Intelligence, 2012
- Chang Wang, Emine Yilmaz, and Martin Szummer, Relevance Feedback Exploiting Query-Specific Document Manifolds, in Conf. Information and Knowledge Management (CIKM), ACM, October 2011
- Martin Szummer and Emine Yilmaz, Semi-supervised Learning to Rank with Preference Regularization, in Conf. Information and Knowledge Management (CIKM), ACM, October 2011
- Nicolas Le Roux, Nicolas Heess, Jamie Shotton, and John Winn, Learning a Generative Model of Images by Factoring Appearance and Shape , in Neural Computation, vol. 23, no. 3, pp. 593-650, MIT Press, March 2011
- Leopold Parts, Oliver Stegle, John Winn, and Richard Durbin, Joint Genetic Analysis of Gene Expression Data with Inferred Cellular Phenotypes, in PLoS Genetics, PLoS, January 2011
- Filip Radlinski, Martin Szummer, and Nick Craswell, Metrics for Assessing Sets of Subtopics, in SIGIR Conf. Research and Development in Information Retrieval, Association for Computing Machinery, Inc., July 2010
- Oliver Stegle, Leopold Parts, Richard Durbin, and John Winn, A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies, in PLoS Computational Biology, PLoS Computational Biology (Public Library of Science Computational Biology), , 6 May 2010
- Filip Radlinski, Martin Szummer, and Nick Craswell, Inferring Query Intent from Reformulations and Clicks, in Proc. 19th Annual International World Wide Web Conference (WWW '10)., Association for Computing Machinery, Inc., April 2010
- A. Simpson, V.Y. Tan, J. Winn, M. Svensen, C.M. Bishop, D.E. Heckerman, I. Buchan, and A. Custovic, Beyond Atopy: Multiple Patterns of Sensitization in Relation to Asthma in a Birth Cohort Study, in Am J Respir Crit Care Med, 18 February 2010
- Alex Mansfield, Peter Gehler, Luc Van Gool, and Carsten Rother, Scene Carving: Scene Consistent Image Retargeting, in ECCV, 2010
- Yoram Bachrach and Ely Porat, Path Disruption Games, in AAMAS 2010, 2010
- Nicolas Le Roux, Nicolas Heess, Jamie Shotton, and John Winn, Learning a generative model of images by factoring appearance and shape, no. MSR-TR-2010-7, January 2010
- Yoram Bachrach, Honor Among Thieves — Collusion in Multi-Unit Auctions, in AAMAS 2010, 2010
- Minh Hoai Nguyen, Lorenzo Torresani, Fernando de la Torre, and Carsten Rother, Weakly supervised discriminative localization and classification: a joint learning process, in ICCV, 2009
- Oliver J. Woodford, Carsten Rother, and Vladimir Kolmogorov, A Global Perspective on MAP Inference for Low-Level Vision, in ICCV, 2009
- Tom Minka and John Winn, Gates, in Advances in Neural Information Processing Systems 21, 2009
- Sara Vicente, Vladimir Kolmogorov, and Carsten Rother, Joint optimization of segmentation and appearance models, in ICCV, 2009