Thore Graepel
PRINCIPAL RESEARCHER
.
I am a researcher at Microsoft Research Cambridge leading the Online Services and Advertising and Applied Games group. Our work is focused on the application of large scale machine learning and probabilistic modelling techniques to a wide range of problems including online advertising, web search, and games. I have a particular passion for the game of go and the quest for developing a go engine that plays as good as the best human players. More recently, I have been investigating crowdsourcing, collective intelligence and social networking data.
Before joining the Cambridge lab of Microsoft Research, I was a postdoctoral researcher at the Department of Computer Science at Royal Holloway, University of London working on learning theory and machine learning algorithms with Prof. John Shawe-Taylor. View the beautiful campus of Royal Holloway here.
Before that, I worked with Nici Schraudolph and Prof. Petros Koumoutsakos as a postdoctoral researcher at the Institute of Computational Science (ICOS) which is part of the Department of Computer Science of the Swiss Federal Institute of Technology, Zürich (ETH). Topics of research were machine learning and large-scale nonlinear optimisation.
I received my doctorate (Dr. rer. nat) from the Department of Computer Science of the Technical University of Berlin, where I was first a member of the Neural Information Processing group of Prof. Klaus Obermayer and later joined the Statistics group of Prof. Ulrich Kockelkorn. My homepage from the good old days at TU Berlin can be found here.
Contact:
Thore Graepel
Microsoft Research Ltd
Roger Needham Building
7 J J Thomson Avenue
Cambridge CB3 0FB, U.K.
Tel. +44 (0)1223 479 759
Fax: +44 (0)1223 479 999
thoreg@microsoft.com
http://research.microsoft.com/en-us/people/thoreg
Publications
- Andrew D. Gordon, Claudio Russo, Marcin Szymczak, Johannes Borgstrom, Nicolas Rolland, Thore Graepel, and Daniel Tarlow, Probabilistic Programs as Spreadsheet Queries, no. MSR-TR-2014-135, November 2014
- Yoram Bachrach, Thore Graepel, Pushmeet Kohli, Michal Kosinski, and David Stillwell, Your Digital Image: Factors Behind Demographic and Psychometric Predictions from Social Network Profiles, in AAMAS, 2014
- Daniel Tarlow, Thore Graepel, and Tom Minka, Knowing what we don't know in NCAA Football ratings: Understanding and using structured uncertainty, MIT Press, 2014
- Andrew D. Gordon, Thore Graepel, Nicolas Rolland, Claudio Russo, Johannes Borgstrom, and John Guiver, Tabular: A Schema-Driven Probabilistic Programming Language, no. MSR-TR-2013-118, 17 December 2013
- Michal Kosinski, David Stillwell, and Thore Graepel, Private traits and attributes are predictable from digital records of human behavior, in Proceedings of the National Academy of Sciences of the United States of America (PNAS), 12 February 2013
- Bin Bi, Milad Shokouhi, Michal Kosinski, and Thore Graepel, Inferring the Demographics of Search Users, in 22nd International World Wide Web Conference, ACM, 2013
- Simon Lacoste-Julien, Konstantina Palla, Alex Davies, Gjergji Kasneci, Thore Graepel, and Zoubin Ghahramani, SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases, in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM International Conference on Knowledge Discovery and Data Mining, 2013
- Andrew D. Gordon, Mihhail Aizatulin, Johannes Borgstroem, Guillaume Claret, Thore Graepel, Aditya V. Nori, Sriram K. Rajamani, and Claudio Russo, A Model-Learner Pattern for Bayesian Reasoning, no. MSR-TR-2013-1, January 2013
- Michal Kosinski, Yoram Bachrach, Pushmeet Kohli, David Stillwell, and Thore Graepel, Manifestations of user personality in website choice and behaviour on online social networks, in Machine Learning, 2013
- Sameer Singh and Thore Graepel, Automated Probabilistic Modelling for Relational Data, in Proceedings of the ACM of Information and Knowledge Management CIKM 2013, ACM, 2013
- Thore Graepel, Kristin Lauter, and Michael Naehrig, ML Confidential: Machine Learning on Encrypted Data, in International Conference on Information Security and Cryptology – ICISC 2012, Lecture Notes in Computer Science, to appear, Springer Verlag, 26 December 2012
- Sameer Singh and Thore Graepel, Compiling Relational Database Schemata into Probabilistic Graphical Models, 5 December 2012
- Tim Salimans, Ulrich Paquet, and Thore Graepel, Collaborative Learning of Preference Relations, in Proceedings of the 6th ACM Conference on Recommender Systems, ACM, May 2012
- Philipp Hennig, David Stern, Ralf Herbrich, and Thore Graepel, Kernel Topic Models, in Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, JMLR W&CP 22: 511-519, Journal of Machine Learning Research, May 2012
- Shengbo Guo, Scott Sanner, Thore Graepel, and Wray Buntine, Score-based Bayesian Skill Learning, in Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD-12), 2012
- Martin Rohrmeier and Thore Graepel, Comparing Feature-Based Models of Harmony, in Proceedings of the 9th International Symposium on Computer Music Modeling and Retrieval CMMR 2012, Springer, 2012
- Yoram Bachrach, Thore Graepel, Gjergji Kasneci, Michal Kosinski, and Jurgen Van Gael, Crowd IQ - Aggregating Opinions to Boost Performance, in AAMAS 2012, 2012
- Michal Kosinski, Yoram Bachrach, Gjergji Kasneci, Jurgen Van-Gael, and Thore Graepel, Crowd IQ: Measuring the Intelligence of Crowdsourcing Platforms, in ACM Web Sciences 2012, ACM Conference on Web Sciences, 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
- Michal Kosinski, David Stillwell, Pushmeet Kohli, Yoram Bachrach, and Thore Graepel, Personality and Website Choice, in ACM Web Sciences 2012, ACM Conference on Web Sciences, 2012
- Yoram Bachrach, Michal Kosinski, Thore Graepel, Pushmeet Kohli, and David Stillwell, Personality and Patterns of Facebook Usage, in ACM Web Sciences 2012, ACM Conference on Web Sciences, 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
- Shengbo Guo, Scott Sanner, Thore Graepel, and Wray Buntine, Score-based Bayesian Skill Learning, in Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD-12), 2012
- Pushmeet Kohli, Yoram Bachrach, David Stillwell, Michael Kearns, Ralf Herbrich, and Thore Graepel, Colonel Blotto On Facebook: The Effect of Social Relations On Strategic Interaction, in ACM Web Sciences 2012, ACM Conference on Web Sciences, 2012
- Scott Sanner, Shengbo Guo, Thore Graepel, Sadegh Kharazmi, and Sarvnaz Karimi, Diverse Retrieval via Greedy Optimization of Expected 1-call@k in a Latent Subtopic Relevance Model, in In Proceedings of CIKM, 20th ACM Conference on Information and Knowledge Management, ACM, 2011
- Weiwei Cheng, Gjergji Kasneci, Thore Graepel, David Stern, and Ralf Herbrich, Automated Feature Generation from Structured Knowledge, in the 20th ACM Conference on Information and Knowledge Management (CIKM 2011), ACM, 2011
- Gjergji Kasneci, Jurgen Van Gael, David Stern, and Thore Graepel, CoBayes: Bayesian Knowledge Corroboration with Assessors of Unknown Areas of Expertise, in the 4th ACM International Conference on Web Search and Data Mining (WSDM2011) , Association for Computing Machinery, Inc., 2011
- Gjergji Kasneci, Jurgen Van Gael, and Thore Graepel, DBrev: Dreaming of a Database Revolution, in the 5th Biennieal Conference on Innovative Datasystems Research (CIDR 2011), Association for Computing Machinery, Inc., 2011
- Yan Xu, Xian Cao, Abigail Sellen, Ralf Herbrich, and Thore Graepel, Sociable killers: understanding social relationships in an online first-person shooter game, in CSCW '11 Proceedings of the ACM 2011 conference on Computer supported cooperative work , ACM, 2011
- Ulrich Paquet, Jurgen Van Gael, David Stern, Gjergji Kasneci, Ralf Herbrich, and Thore Graepel, Vuvuzelas & Active Learning for Online Classification, in Computational Social Science and the Wisdom of Crowds Workshop (colocated with NIPS 2010), December 2010
- 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
- Gjergji Kasneci, Jurgen Van Gael, Ralf Herbrich, and Thore Graepel, Bayesian Knowledge Corroboration with Logical Rules and User Feedback, no. MSR-TR-2010-45, 6 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
- 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
- Gjergji Kasneci, Jurgen V. Gael, Ralf Herbrich, and Thore Graepel, Bayesian Knowledge Corroboration with Logical Rules and User Feedback, in Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Springer Verlag, 2010
- Peter A. Flach, John Guiver, Mohammed J. Zaki, Sebastian Spiegler, Bruno Golenia, Ralf Herbrich, Thore Graepel, and Simon Price, Novel Tools To Streamline the Conference Review Process: Experiences from SIGKDD'09, in SIGKDD Explorations, vol. 11, no. (2), Association for Computing Machinery, Inc., December 2009
- David Stern, Ralf Herbrich, and Thore Graepel, Matchbox: Large Scale Bayesian Recommendations, in Proceedings of the 18th International World Wide Web Conference, 2009
- Anton Schwaighofer, Joaquin Quinonero Candela, Thomas Borchert, Thore Graepel, and Ralf Herbrich, Scalable Clustering and Keyword Suggestion for Online Advertisements, in Proceedings of ADKDD 2009: 3rd Annual International Workshop on Data Mining and Audience Intelligence for Advertising, Association for Computing Machinery, Inc., 2009
- 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, Thore Graepel, and Brendan Murphy, Structure from Failure, in Second Workshop on Tackling Computer Systems Problems with Machine Learning Techniques (SysML07) , USENIX, June 2007
- 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
- David Stern, Ralf Herbrich, and Thore Graepel, Learning To Solve Game Trees, in Proceedings of the International Conference of Machine Learning, January 2007
- Thore Graepel and Ralf Herbrich, Ranking and Matchmaking, in Game Developer Magazine, October 2006
- Ralf Herbrich and Thore Graepel, TrueSkill(TM): A Bayesian Skill Rating System, no. MSR-TR-2006-80, 2006
- David Stern, Ralf Herbrich, and Thore Graepel, Bayesian Pattern Ranking for Move Prediction in the Game of Go, in Proceedings of the International Conference of Machine Learning, January 2006
- Shyamsundar Rajaram, Thore Graepel, and Ralf Herbrich, Poisson-Networks: A Model for structured point processes, in Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, January 2005
- Shivani Agarwal, Thore Graepel, Ralf Herbrich, Sariel Har-Peled, and Dan Roth, Generalization Error Bounds for the Area Under the ROC curve, in Journal of Machine Learning Research, vol. 6, pp. 393-425, MIT Press, January 2005
- Ralf Herbrich, Thore Graepel, and Robert C. Williamson, The Structure of Version Space, in Innovations in Machine Learning: Theory and Applications, pp. 257-274, Springer-Verlag, 2005
- Thore Graepel, Ralf Herbrich, and John Shawe-Taylor, PAC-Bayesian compression bounds on the prediction error of learning algorithms for classification, in Machine Learning, vol. 59, pp. 55-76, Kluwer Academic , January 2005
- Ralf Herbrich, Thore Graepel, and Robert C. Williamson, The Structure of Version Space, no. MSR-TR-2004-63, July 2004
- Shivani Agarwal, Thore Graepel, Ralf Herbrich, and Dan Roth, A Large Deviation Bound for the Area Under the ROC Curve, in Advances in Neural Information Processing Systems 17, MIT Press, January 2004
- Hendrik Purwins, Thore Graepel, Benjamin Blankertz, and Klaus Obermayer, Correspondence analysis for visualizing interplay of pitch class, key, and composer, in Perspectives in Mathematical and Computational Music Theory, pp. 432 - 454, Universities Press, 2004
- Thore Graepel, Ralf Herbrich, Andriy Kharechko, and John Shawe-Taylor, Semidefinite Programming by Perceptron Learning, in Advances in Neural Information Processing Systems 16, MIT Press, January 2004
- Thore Graepel, Ralf Herbrich, and Julian Gold, Learning to Fight, in Proceedings of the International Conference on Computer Games: Artificial Intelligence, Design and Education, January 2004
- Thore Graepel and Ralf Herbrich, Invariant Pattern Recognition by Semidefinite Programming Machines, in Advances in Neural Information Processing Systems 16, MIT Press, January 2004
- David Stern, Thore Graepel, and David MacKay, Modelling Uncertainty in the Game of Go, in Advances in Neural Information Processing Systems 16, January 2004
- Ralf Herbrich and Thore Graepel, Introduction to the Special Issue on Learning Theory, in Journal of Machine Learning Research, vol. 4, pp. 755–757, MIT Press, October 2003
- Thore Graepel, Solving Noisy Linear Operator Equations by Gaussian Processes: Application to Ordinary and Partial Differential Equations, in Proceedings of the Twentieth International Conference on Machine Learning, January 2003
- Malte Kuss and Thore Graepel, The Geometry of Kernel Canonical Correlation Analysis, no. 108, January 2003
- Nicol N. Schraudolph and Thore Graepel, Combining Conjugate Direction Methods with Stochastic Approximation of Gradients, in Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, AISTATS 2003, January 2003
- Ralf Herbrich and Thore Graepel, A PAC-Bayesian Margin Bound for Linear Classifiers, in IEEE Transactions on Information Theory, vol. 48, no. 12, pp. 3140–3150, January 2002
- Thore Graepel, Kernel matrix completion by semidefinite programming, in Proceedings of the International Conference on Neural Networks, ICANN 2002, Springer, January 2002
- Nicol N. Schraudolph and Thore Graepel, Towards Stochastic Conjugate Gradient Methods, in Proceedings of the 9th International Conference on Neural Information Processing, ICONIP 2002, January 2002
- Thore Graepel and Nicol N. Schraudolph, Stable adaptive momentum for rapid online learning in nonlinear systems, in Proceedings of the International Conference on Neural Networks, ICANN 2002, Springer, January 2002
- Nicol N. Schraudolph and Thore Graepel, Conjugate Directions for Stochastic Gradient Descent, in Proceedings of the International Conference on Neural Networks, ICANN 2002, Springer, January 2002
- Ralf Herbrich, Thore Graepel, and Colin Campbell, Bayes Point Machines, in Journal of Machine Learning Research, vol. 1, pp. 245-279, MIT Press, January 2001
- Thore Graepel and Ralf Herbrich, A PAC-Bayesian Margin Distribution Bound for Kernel Classifiers (extended abstract), January 2001
- Sambu Seo, Marko Wallat, Thore Graepel, and Klaus Obermayer, Gaussian Process Regression: Active Data Selection and Test Point Rejection, in Proceedings of the International Joint Conference on Neural Networks IJCNN'2000, January 2000
- Thore Graepel and Klaus Obermayer, A Self-Organizing Map for Proximity Data, in Neural Computation, vol. 11, pp. 139-155, January 1999
- Thore Graepel, Statistical Physics of Clustering Algorithms, Berlin, Germany, January 1998
- Thore Graepel, Matthias Burger, and Klaus Obermayer, Self-Organizing Maps: Generalizations and New Optimization Techniques, in Neurocomputing, vol. 20, pp. 173-190, January 1998
- Matthias Burger, Thore Graepel, and Klaus Obermayer, Phase Transitions in Soft Topographic Vector Quantization, in Artificial Neural Networks – ICANN97, Springer-Verlag, January 1997
- Thore Graepel, Matthias Burger, and Klaus Obermayer, Deterministic Annealing for Topographic Vector Quantization and Self-Organizing Maps, in Proceedings of the Workshop on Self-Organizing Maps WSOM`97, January 1997
- Thore Graepel, Matthias Burger, and Klaus Obermayer, Phase Transitions in Stochastic Self-Organizing Maps, in Physical Review E, vol. 56, no. 4, pp. 3876-3890, January 1997