2013

• A. Montillo, J. Tu, J. Shotton, J. Winn, J.E. Iglesias, D.N. Metaxas, and A. Criminisi, Entanglement and Differentiable Information Gain Maximization, in Decision Forests for Computer Vision and Medical Image Analysis, Springer, 2013

2012

• 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
• Theofanis Karaletsos, Oliver Stegle, Christine Dreyer, John Winn, and Karsten M. Borgwardt, ShapePheno: Unsupervised extraction of shape phenotypes from biological image collections, in Bioinformatics, pp. 1001-1008, Oxford University Press, February 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
• L. Parts, Å.K. Hedman, S. Keildson, A.J. Knights, C. Abreu-Goodger, M. van de Bunt, J.A. Guerra-Assunção, N. Bartonicek, S. van Dongen, R. Mägi, J. Nesbit, A. Barrett, M. Rantalainen, A. C. Nica, M. A. Quail, K. S. Small, D. Glass, A. J. Enright, J. Winn, P. Deloukas, E. T. Dermitzakis, M. I. McCarthy, T. D. Spector, R. Durbin, and C. M. Lindgren, Extent, Causes, and Consequences of Small RNA Expression Variation in Human Adipose Tissue, in PLoS Genetics, vol. 8, no. 5, pp. e1002704, Public Library of Science, 2012

2011

• A. Montillo, J. Shotton, J. Winn, J. E. Iglesias, D. Metaxas, and A. Criminisi, Entangled Decision Forests and their Application for Semantic Segmentation of CT Images, in Information Processing in Medical Imaging (IPMI), July 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
• Nicolas Heess, Nicolas Le Roux, and John Winn, Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs, in International Conference on Artificial Neural Networks, 2011
• John Winn and Jamie Shotton, Markov Random Fields for Object Detection, in Markov Random Fields for Vision and Image Processing, pp. 389-404, MIT Press, 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

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
• 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
• 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
• A. Criminisi, A. Montillo, J. Shotton, J. Winn, S. Pathak, and K. Siddiqui, Automatic Semantic Segmentation of Anatomical Structures in CT Scans, in Radiological Society of North America (RSNA), 2010
• D. Knowles, L. Parts, D. Glass, and J. Winn, Modeling skin and ageing phenotypes using latent variable models in Infer.NET, in Predictive Models in Personalized Medicine Workshop, NIPS, , 2010
• Pei Yin, Antonio Criminisi, John Winn, and Irfan Essa, Bilayer Segmentation of Webcam Videos Using Tree-based Classifiers, in Trans. Pattern Analysis and Machine Intelligence (PAMI), IEEE, 2010

2009

• Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John Winn, and Andrew Zisserman, The Pascal Visual Object Classes (VOC) Challenge, in International Journal of Computer Vision, vol. 88, no. 2, pp. 303-308, Springer Verlag, 9 September 2009
• Tom Minka and John Winn, Gates, in Advances in Neural Information Processing Systems 21, 2009
• Magnus Rattray, Oliver Stegle, Kevin Sharp, and John Winn, Inference algorithms and learning theory for Bayesian sparse factor analysis, in International Workshop on Statistical-Mechanical Informatics 2009, Journal of Physics: Conference Series, 2009
• Jamie Shotton, John Winn, Carsten Rother, and Antonio Criminisi, TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context, in Int. Journal of Computer Vision (IJCV), Springer Verlag, January 2009
• Kai Ni, Anitha Kannan, Antonio Criminisi, and John Winn, Epitomic Location Recognition, in IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI special issue), IEEE, 2009
• Iain Buchan, John Winn, and Christopher Bishop, A Unified Modeling Approach to Data-Intensive Healthcare, in The Fourth Paradigm: Data-Intensive Scientific Discovery, Microsoft Research, 2009

2008

• Tom Minka and John Winn, Gates: A graphical notation for mixture models, no. MSR-TR-2008-185, 5 December 2008
• Vincent Y. F. Tan, John Winn, Angela Simpson, and Adnan Custovic, Immune System Modeling with Infer.NET, in IEEE International Conference on e-Science (e-Science 2008), , Indianapolis, Indiana, 1 December 2008
• Oliver Stegle, Anitha Kannan, Richard Durbin, and John M. Winn, Accounting for Non-genetic Factors Improves the Power of eQTL Studies, in International Conference on Research in Computational Molecular Biology, 2008
• Kai Ni, Anitha Kannan, Antonio Criminisi, and John Winn, Epitomic Location Recognition, in Proc IEEE Conference on Computer Vision (CVPR). Winner of BEST STUDENT PAPER RUNNER UP AWARD., IEEE Computer Society, 2008

2007

• Jean Francois Lalonde, Derek Hoiem, Alyosha A Efros, John Winn, Carsten Rother, and Antonio Criminisi, Photo Clip Art, in Proc. ACM SIGGRAPH, 2007
• Anitha Kannan, John Winn, and Carsten Rother, Clustering appearance and shape by learning jigsaws, in Advances in Neural Information Processing Systems, MIT Press, 2007
• Shahram Izadi, Ankur Agarwal, Antonio Criminisi, John Winn, Andrew Blake, and Andrew Fitzgibbon, C-Slate: A Multi-Touch and Object Recognition System for Remote Collaboration using Horizontal Surfaces, in Proceedings of the Second Annual IEEE International Workshop on Horizontal Interactive Human-Computer Systems (Tabletop 2007), IEEE, 2007
• D. Hoeim, C. Rother, and J. Winn, 3D LayoutCRF for Multi-View Object Class Recognition and Segmentation , in Proc. IEEE Computer Vision and Pattern Recognition (CVPR) , Minneapolis, US, 2007
• Pei Yin, Antonio Criminisi, Irfan Essa, and John Winn, Tree-based Classifiers for Bilayer Video Segmentation, in Proc. Conf. Computer Vision and Pattern Recognition, 2007
• Jim C. Huang, Anitha Kannan, and John M. Winn, Bayesian association of haplotypes and non-genetic factors to regulatory and phenotypic variation in human populations, in International Conference on Intelligent Systems for Molecular Biology, 2007
• Shahram Izadi, Ankur Agarwal, Antonio Criminisi, John Winn, Andrew Blake, and Andrew Fitzgibbon, C-Slate: Exploring Remote Collaboration on Horizontal Multi-touch Surfaces. , in Proc. IEEE Tabletop, 2007
• Julia Lasserre, Anitha Kannan, and John Winn, Hybrid learning of large jigsaws, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007
• Thomas Deselaers, Antonio Criminisi, John Winn, and Ankur Agarwal, Incorporating On-demand Stereo for Real Time Recognition, in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2007

2006

• John Winn and Jamie Shotton, The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects, in Proceedings of IEEE CVPR, January 2006
• Jamie Shotton, John Winn, Carsten Rother, and Antonio Criminisi, TextonBoost: Joint Appearance, Shape and Context Modeling for Mulit-Class Object Recognition and Segmentation, in European Conference on Computer Vision (ECCV), January 2006
• Nebojsa Jojic, John Winn, and Larry Zitnick, Escaping Local Minima through Hierarchical Model Selection: Automatic Object Discovery, Segmentation, and Tracking in Video, in Proceedings of IEEE CVPR, January 2006
• Anitha Kannan, John Winn, and Carsten Rother, Clustering appearance and shape by learning jigsaws, in NIPS, 2006
• Ashish Kapoor and John Winn, Located Hidden Random Fields Learning Discriminative Parts for Object Detection, in European Conference on Computer Vision, January 2006
• John Winn and Antonio Criminisi, Object Class Recognition at a Glance, in Proc. Conf. Computer Vision and Pattern Recognition (CVPR) -- Video Track, 2006
• Silvio Savarese, Antonio Criminisi, and John Winn, Discriminative Object Class Models of Appearance and Shape by Correlatons, in Proc. IEEE Computer Vision and Pattern Recognition (CVPR)., January 2006

2005

• John Winn, Antonio Criminisi, and Thomas Minka, Object Categorization by Learned Universal Visual Dictionary, in Proc. IEEE Intl. Conf. on Computer Vision (ICCV)., January 2005
• John Winn and Nebojsa Jojic, LOCUS: Learning Object Classes with Unsupervised Segmentation, in Proc. IEEE Intl. Conf. on Computer Vision (ICCV), January 2005

2004

• John Winn and Andrew Blake, Generative Affine Localisation and Tracking, in Advances in Neural Information Processing Systems, January 2004
• John Winn and Christopher M. Bishop, Variational Message Passing, in Journal of Machine Learning Research, vol. 5, January 2004

2003

• C. M. Bishop and J. M. Winn, Structured variational distributions in VIBES, in Proceedings Artificial Intelligence and Statistics, Society for Artificial Intelligence and Statistics, January 2003

2002

• C. M. Bishop, J. M. Winn, and D. Spiegelhalter, VIBES: A variational inference engine for Bayesian networks, in Advances in Neural Information Processing Systems, January 2002

2000

• C. M. Bishop and J. M. Winn, Non-linear Bayesian image modelling, in Proceedings Sixth European Conference on Computer Vision, Springer-Verlag, January 2000