Bishop, Christopher M.

29 publications

ECML-PKDD 2014 Students, Teachers, Exams and MOOCs: Predicting and Optimizing Attainment in Web-Based Education Using a Probabilistic Graphical Model Bar Shalem, Yoram Bachrach, John Guiver, Christopher M. Bishop

AISTATS 2013 Structural Expectation Propagation (SEP): Bayesian Structure Learning for Networks with Latent Variables Nevena Lazic, Christopher M. Bishop, John M. Winn

ECML-PKDD 2011 Embracing Uncertainty: Applied Machine Learning Comes of Age Christopher M. Bishop

CVPR 2006 Principled Hybrids of Generative and Discriminative Models Julia A. Lasserre, Christopher M. Bishop, Thomas P. Minka

CVPR 2005 Generative Versus Discriminative Methods for Object Recognition Ilkay Ulusoy, Christopher M. Bishop

JMLR 2005 Variational Message Passing John Winn, Christopher M. Bishop

UAI 2003 Bayesian Hierarchical Mixtures of Experts Christopher M. Bishop, Markus Svensén

AISTATS 2003 Structured Variational Distributions in VIBES Christopher M. Bishop, John M. Winn

AISTATS 2003 Super-Resolution Enhancement of Video Christopher M. Bishop, Andrew Blake, Bhaskara Marthi

NeurIPS 2002 Bayesian Image Super-Resolution Michael E. Tipping, Christopher M. Bishop

NeurIPS 2002 VIBES: A Variational Inference Engine for Bayesian Networks Christopher M. Bishop, David Spiegelhalter, John Winn

AISTATS 2001 Hyperparameters for Soft Bayesian Model Selection Adrian Corduneanu, Christopher M. Bishop

NeurIPS 2001 Optimising Synchronisation Times for Mobile Devices Neil D. Lawrence, Antony I. T. Rowstron, Christopher M. Bishop, Michael J. Taylor

ECCV 2000 Non-Linear Bayesian Image Modelling Christopher M. Bishop, John M. Winn

UAI 2000 Variational Relevance Vector Machines Christopher M. Bishop, Michael E. Tipping

NeCo 1999 Mixtures of Probabilistic Principal Component Analysers Michael E. Tipping, Christopher M. Bishop

NeurIPS 1998 Bayesian PCA Christopher M. Bishop

NeCo 1998 GTM: The Generative Topographic Mapping Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams

UAI 1998 Mixture Representations for Inference and Learning in Boltzmann Machines Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan

NeurIPS 1997 Approximating Posterior Distributions in Belief Networks Using Mixtures Christopher M. Bishop, Neil D. Lawrence, Tommi Jaakkola, Michael I. Jordan

NeurIPS 1997 Ensemble Learning for Multi-Layer Networks David Barber, Christopher M. Bishop

NeurIPS 1997 Regression with Input-Dependent Noise: A Gaussian Process Treatment Paul W. Goldberg, Christopher K. I. Williams, Christopher M. Bishop

NeurIPS 1996 Bayesian Model Comparison by Monte Carlo Chaining David Barber, Christopher M. Bishop

NeurIPS 1996 GTM: A Principled Alternative to the Self-Organizing mAP Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams

NeCo 1996 Modeling Conditional Probability Distributions for Periodic Variables Christopher M. Bishop, Ian T. Nabney

NeurIPS 1996 Regression with Input-Dependent Noise: A Bayesian Treatment Christopher M. Bishop, Cazhaow S. Quazaz

NeurIPS 1995 EM Optimization of Latent-Variable Density Models Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams

NeCo 1995 Real-Time Control of a Tokamak Plasma Using Neural Networks Christopher M. Bishop, Paul S. Haynes, Mike E. U. Smith, Tom N. Todd, David L. Trotman

NeCo 1995 Training with Noise Is Equivalent to Tikhonov Regularization Christopher M. Bishop