Machine learning is one of the major research themes in the Machine Intelligence & Perception group in Cambridge. We develop new machine learning methods, taking them from theory to generic computer implementations. We also work on practical applications of these new methods, as well as more established methods in new domains.
- Infer.NET – 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.
- 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 Intelligence and Perception
> Machine Learning at MSR Cambridge
- Yoram Bachrach and Ely Porat, Fingerprints for Highly Similar Streams, in Information and Computation, Elsevier, October 2015.
- Yoad Lewenberg, Yoram Bachrach, and Svitlana Volkova, Using Emotions to Predict User Interest Areas in Online Social Networks, in DSAA (Data Science and Advanced Anayltics), ACM – Association for Computing Machinery, October 2015.
- Yoram Bachrach, Human Judgments In Hiring Decisions Based On Online Social Network Profiles, in DSAA (Data Science and Advanced Analytics), ACM – Association for Computing Machinery, October 2015.
- Daniel Preotiuc-Pietro, Svitlana Volkova, Vasileios Lampos, Yoram Bachrach, and Nikolaos Aletras, Studying User Income through Language, Behaviour and Affect in Social Media, in PLOS One, PLOS – Public Library of Science, September 2015.
- Cheng Zhang, Mike Gartrell, Thomas P. Minka, Yordan Zaykov, and John Guiver, GroupBox: A generative model for group recommendation, no. MSR-TR-2015-61, July 2015.
- Dan Alistarh, Thomas Sauerwald, and Milan Vojnovic, Lock-Free Algorithms under Stochastic Schedulers, no. MSR-TR-2015-41, May 2015.
- Yoad Lewenberg, Yoram Bachrach, Yonatan Sompolinsky, Aviv Zohar, and Jeffrey S. Rosenschein, Bitcoin Mining Pools: A Cooperative Game Theoretic Analysis, in AAMAS, May 2015.
- Toby Sharp, Cem Keskin, Duncan Robertson, Jonathan Taylor, Jamie Shotton, David Kim, Christoph Rhemann, Ido Leichter, Alon Vinnikov, Yichen Wei, Daniel Freedman, Pushmeet Kohli, Eyal Krupka, Andrew Fitzgibbon, and Shahram Izadi, Accurate, Robust, and Flexible Real-time Hand Tracking, CHI, April 2015.
- Frank Kelly, Peter Key, and Neil Walton, Efficient Advert Assignment, 12 September 2014.
- Bar Shalem, yobach, John Guiver, and Chris Bishop, Students, Teachers, Exams and MOOCs: Predicting and Optimizing Attainment in Web-Based Education Using a Probabilistic Graphical Model, ECML/PKDD, September 2014.
- Yoram Bachrach, Sofia Ceppi, Ian A. Kash, Peter Key, and David Kurokawa, Optimising Trade-offs Among Stakeholders in Ad Auctions, in EC'14, 15th ACM Conference on Economics and Computation, Stanford, CA, USA, ACM, June 2014.
- Matteo Venanzi, John Guiver, Gabriella Kazai, Pushmeet Kohli, and Milad Shokouhi, Community-Based Bayesian Aggregation Models for Crowdsourcing, in Proceedings of the 23rd International World Wide Web Conference, WWW2014 (Best paper award runner up), ACM, April 2014.
- 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.
- 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.
- 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.
- 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.
- Martin Szummer and Emine Yilmaz, Semi-supervised Learning to Rank with Preference Regularization, in Conf. Information and Knowledge Management (CIKM), ACM, October 2011.
- Chang Wang, Emine Yilmaz, and Martin Szummer, Relevance Feedback Exploiting Query-Specific Document Manifolds, 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.