Machine learning is a key technology in the area of online services and our work focuses on principled machine learning methods at web scale. Our expertise includes kernel methods, learning theory and graphical models. The drivers behind our research are applications in online services and our work is aimed at understanding those aspects of machine learning that can help make use of the vast amount of data available online.
- Data-centric Modelling Languages: We are developing systems to facilitate rapid prototyping and deployment of graphical models on large scale data by aligning the modelling language with the data representation. Current work is focussing on a probabilistic query language a la SQL which we call PQL.
- Inference in Web-Scale Graphical Models: Inference in large scale graphical models requires distributed computations. We develop algorithms for distributed inference that carry out message passing based inference on distributed factor graphs with an eye on balancing approximation accuracy with computational constraints.
- Machine Learning in Closed Loop Systems: In online services such as search and advertising the predictions of machine learning algorithms are used to drive decisions about what to display. These decisions determine the future composition of the training sample for the machine learning algorithm. We investigate aspects of dynamics, exploration, and incentives related to this closed feedback loop.
- Learning to Rank:Ranking is a fundamental task in information retrieval and skill estimation from game outcomes. We investigate learning algorithms for this task with a focus on graphical models.
- David Stern, Ralf Herbrich, Thore Graepel, Horst Samulowitz, Luca Pulina, and Armando Tacchella, Collaborative Expert Portfolio Management, in Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence AAAI-10 (to appear), July 2010.
- Thore Graepel, Joaquin Quinonero Candela, Thomas Borchert, and Ralf Herbrich, Web-Scale Bayesian Click-Through Rate Prediction for Sponsored Search Advertising in Microsoft’s Bing Search Engine, in Proceedings of the 27th International Conference on Machine Learning ICML 2010, Invited Applications Track (unreviewed, to appear), June 2010.
- Xinhua Zhang, Thore Graepel, and Ralf Herbrich, Bayesian Online Learning for Multi-Label and Multi-Variate Performance Measures, in Proceedings of the Thirteenth Conference on Artificial Intelligence and Statistics AISTATS 2010 (to appear), May 2010.
- Philipp Hennig, David Stern, and Thore Graepel, Coherent Inference on Optimal Play in Game Trees, in Proceedings of the Thirteenth Conference on Artificial Intelligence and Statistics AISTATS 2010 (to appear), May 2010.
- Michael Taylor, John Guiver, Stephen Robertson, and Tom Minka, SoftRank: Optimising Non-Smooth Rank Metrics, in WSDM 2008, February 2008.
- Pierre Dangauthier, Ralf Herbrich, Tom Minka, and Thore Graepel, TrueSkill Through Time: Revisiting the History of Chess, in Advances in Neural Information Processing Systems 20, MIT Press, 2008.
- Ralf Herbrich, Tom Minka, and Thore Graepel, TrueSkill(TM): A Bayesian Skill Rating System, in Advances in Neural Information Processing Systems 20, MIT Press, January 2007.
- Ralf Herbrich and Thore Graepel, TrueSkill(TM): A Bayesian Skill Rating System, no. MSR-TR-2006-80, 2006.
- Ralf Herbrich, On Gaussian Expectation Propagation, July 2005.
- Ralf Herbrich, Thore Graepel, and Klaus Obermayer, Large Margin Rank Boundaries for Ordinal Regression, pp. 115–132, MIT Press, January 2000.