Christopher K. I. Williams

156 papers A* 4A 15B 4C 1Misc 1Journal 82Unranked 43

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Fusing Foveal Fixations Using Linear Retinal Transformations and Bayesian Experimental Design. CoRR Christopher K. I. Williams
2025	J	`jnl`	Fusing Foveal Fixations Using Linear Retinal Transformations and Bayesian Experimental Design. Neural Comput. Christopher K. I. Williams
2025	J	`jnl`	Structured Generative Models for Scene Understanding. Int. J. Comput. Vis. Christopher K. I. Williams
2024	J	`jnl`	Naive Bayes Classifiers and One-hot Encoding of Categorical Variables. CoRR Christopher K. I. Williams
2024	J	`jnl`	Of Mice and Mates: Automated Classification and Modelling of Mouse Behaviour in Groups Using a Single Model Across Cages. Int. J. Comput. Vis. Michael P. J. Camilleri, Rasneer S. Bains, Christopher K. I. Williams
2023	J	`jnl`	AI Assistants: A Framework for Semi-Automated Data Wrangling. IEEE Trans. Knowl. Data Eng. Tomas Petricek, Gerrit J. J. van den Burg, Alfredo Nazábal, Taha Ceritli, Ernesto Jiménez-Ruiz, Christopher K. I. Williams
2023	J	`jnl`	Inference and Learning for Generative Capsule Models. Neural Comput. Alfredo Nazábal, Nikolaos Tsagkas, Christopher K. I. Williams
2023	J	`jnl`	Of Mice and Mates: Automated Classification and Modelling of Mouse Behaviour in Groups using a Single Model across Cages. CoRR Michael P. J. Camilleri, Rasneer S. Bains, Christopher K. I. Williams
2023	J	`jnl`	Persistent animal identification leveraging non-visual markers. Mach. Vis. Appl. Michael P. J. Camilleri, Li Zhang, Rasneer S. Bains, Andrew Zisserman, Christopher K. I. Williams
2023	J	`jnl`	Structured Generative Models for Scene Understanding. CoRR Christopher K. I. Williams
2023	J	`jnl`	The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence. CoRR Hector Zenil, Jesper Tegnér, Felipe S. Abrahão, Alexander Lavin, Vipin Kumar, Jeremy G. Frey, Adrian Weller, Larisa N. Soldatova, Alan R. Bundy, Nicholas R. Jennings, Koichi Takahashi, Lawrence Hunter, Saso Dzeroski, Andrew Briggs, Frederick D. Gregory, Carla P. Gomes, Christopher K. I. Williams, Jonathan E. Rowe, James A. Evans, Hiroaki Kitano, Joshua B. Tenenbaum, Ross D. King
2022	J	`jnl`	AI Assistants: A Framework for Semi-Automated Data Wrangling. CoRR Tomas Petricek, Gerrit J. J. van den Burg, Alfredo Nazábal, Taha Ceritli, Ernesto Jiménez-Ruiz, Christopher K. I. Williams
2022	J	`jnl`	Align-Deform-Subtract: An Interventional Framework for Explaining Object Differences. CoRR Cian Eastwood, Nanbo Li, Christopher K. I. Williams
2022	J	`jnl`	Automating data science. Commun. ACM Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams
2022	J	`jnl`	Inference and Learning for Generative Capsule Models. CoRR Alfredo Nazábal, Nikolaos Tsagkas, Christopher K. I. Williams
2022	J	`jnl`	Multi-Task Dynamical Systems. CoRR Alex Bird, Christopher K. I. Williams, Christopher Hawthorne
2022	J	`jnl`	Multi-Task Dynamical Systems. J. Mach. Learn. Res. Alex Bird, Christopher K. I. Williams, Christopher Hawthorne
2022	J	`jnl`	On Suspicious Coincidences and Pointwise Mutual Information. CoRR Christopher K. I. Williams
2022	J	`jnl`	On Suspicious Coincidences and Pointwise Mutual Information. Neural Comput. Christopher K. I. Williams
2022	A*	`conf`	Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration. ICLR Cian Eastwood, Ian Mason, Christopher K. I. Williams, Bernhard Schölkopf
2022	J	`jnl`	The Elliptical Quartic Exponential Distribution: An Annular Distribution Obtained via Maximum Entropy. CoRR Christopher K. I. Williams
2021	J	`jnl`	Automating Data Science: Prospects and Challenges. CoRR Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams
2021	J	`jnl`	Identifying the Units of Measurement in Tabular Data. CoRR Taha Ceritli, Christopher K. I. Williams
2021	J	`jnl`	Inference for Generative Capsule Models. CoRR Alfredo Nazábal, Christopher K. I. Williams
2021	A*	`conf`	On Memorization in Probabilistic Deep Generative Models. NeurIPS Gerrit J. J. van den Burg, Christopher K. I. Williams
2021	J	`jnl`	On Memorization in Probabilistic Deep Generative Models. CoRR Gerrit J. J. van den Burg, Christopher K. I. Williams
2021	J	`jnl`	Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration. CoRR Cian Eastwood, Ian Mason, Christopher K. I. Williams, Bernhard Schölkopf
2021	J	`jnl`	The Effect of Class Imbalance on Precision-Recall Curves. Neural Comput. Christopher K. I. Williams
2021	J	`jnl`	Tracking and Long-Term Identification Using Non-Visual Markers. CoRR Michael P. J. Camilleri, Li Zhang, Rasneer S. Bains, Andrew Zisserman, Christopher K. I. Williams
2021	—	`conf`	Unit-level surprise in neural networks. ICBINB@NeurIPS Cian Eastwood, Ian Mason, Christopher K. I. Williams
2021	J	`jnl`	ptype-cat: Inferring the Type and Values of Categorical Variables. CoRR Taha Ceritli, Christopher K. I. Williams
2020	J	`jnl`	An Evaluation of Change Point Detection Algorithms. CoRR Gerrit J. J. van den Burg, Christopher K. I. Williams
2020	J	`jnl`	Data Engineering for Data Analytics: A Classification of the Issues, and Case Studies. CoRR Alfredo Nazábal, Christopher K. I. Williams, Giovanni Colavizza, Camila Rangel Smith, Angus Williams
2020	J	`jnl`	Learning Direct Optimization for scene understanding. Pattern Recognit. Lukasz Romaszko, Christopher K. I. Williams, John M. Winn
2020	A	`conf`	Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data. AISTATS Simão Eduardo, Alfredo Nazábal, Christopher K. I. Williams, Charles Sutton
2020	J	`jnl`	The Effect of Class Imbalance on Precision-Recall Curves. CoRR Christopher K. I. Williams
2020	J	`jnl`	VAEs in the Presence of Missing Data. CoRR Mark Collier, Alfredo Nazábal, Christopher K. I. Williams
2020	J	`jnl`	ptype: probabilistic type inference. Data Min. Knowl. Discov. Taha Ceritli, Christopher K. I. Williams, James Geddes
2019	J	`jnl`	Customizing Sequence Generation with Multi-Task Dynamical Systems. CoRR Alex Bird, Christopher K. I. Williams
2019	A	`conf`	Inverting Supervised Representations with Autoregressive Neural Density Models. AISTATS Charlie Nash, Nate Kushman, Christopher K. I. Williams
2019	A	`conf`	Multi-Task Time Series Analysis applied to Drug Response Modelling. AISTATS Alex Bird, Christopher K. I. Williams, Christopher Hawthorne
2019	J	`jnl`	Multi-Task Time Series Analysis applied to Drug Response Modelling. CoRR Alex Bird, Christopher K. I. Williams, Christopher Hawthorne
2019	J	`jnl`	Robust Variational Autoencoders for Outlier Detection in Mixed-Type Data. CoRR Simão Eduardo, Alfredo Nazábal, Christopher K. I. Williams, Charles Sutton
2019	J	`jnl`	The Extended Dawid-Skene Model: Fusing Information from Multiple Data Schemas. CoRR Michael P. J. Camilleri, Christopher K. I. Williams
2019	J	`jnl`	ptype: Probabilistic Type Inference. CoRR Taha Ceritli, Christopher K. I. Williams, James Geddes
2018	—	`conf`	A Framework for the Quantitative Evaluation of Disentangled Representations. ICLR (Poster) Cian Eastwood, Christopher K. I. Williams
2018	J	`jnl`	Autoencoders and Probabilistic Inference with Missing Data: An Exact Solution for The Factor Analysis Case. CoRR Christopher K. I. Williams, Charlie Nash
2018	J	`jnl`	Estimating Bacterial and Cellular Load in FCFM Imaging. J. Imaging Sohan Seth, Ahsan R. Akram, Kevin Dhaliwal, Christopher K. I. Williams
2018	J	`jnl`	Inverting Supervised Representations with Autoregressive Neural Density Models. CoRR Charlie Nash, Nate Kushman, Christopher K. I. Williams
2018	J	`jnl`	Learning Direct Optimization for Scene Understanding. CoRR Lukasz Romaszko, Christopher K. I. Williams, John M. Winn
2017	—	`conf`	Estimating Bacterial Load in FCFM Imaging. MIUA Sohan Seth, Ahsan R. Akram, Kevin Dhaliwal, Christopher K. I. Williams
2017	J	`jnl`	The shape variational autoencoder: A deep generative model of part-segmented 3D objects. Comput. Graph. Forum Charlie Nash, Christopher K. I. Williams
2017	—	`conf`	Vision-as-Inverse-Graphics: Obtaining a Rich 3D Explanation of a Scene from a Single Image. ICCV Workshops Lukasz Romaszko, Christopher K. I. Williams, Pol Moreno, Pushmeet Kohli
2016	—	`conf`	Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring. MLHC Konstantinos Georgatzis, Christopher K. I. Williams, Christopher Hawthorne
2016	J	`jnl`	Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring. CoRR Konstantinos Georgatzis, Christopher K. I. Williams, Christopher Hawthorne
2016	—	`conf`	Overcoming Occlusion with Inverse Graphics. ECCV Workshops (3) Pol Moreno, Christopher K. I. Williams, Charlie Nash, Pushmeet Kohli
2016	J	`jnl`	Predicting Patient State-of-Health using Sliding Window and Recurrent Classifiers. CoRR Adam McCarthy, Christopher K. I. Williams
2015	A	`conf`	Discriminative Switching Linear Dynamical Systems applied to Physiological Condition Monitoring. UAI Konstantinos Georgatzis, Christopher K. I. Williams
2015	J	`jnl`	Discriminative Switching Linear Dynamical Systems applied to Physiological Condition Monitoring. CoRR Konstantinos Georgatzis, Christopher K. I. Williams
2015	J	`jnl`	The Pascal Visual Object Classes Challenge: A Retrospective. Int. J. Comput. Vis. Mark Everingham, S. M. Ali Eslami, Luc Van Gool, Christopher K. I. Williams, John M. Winn, Andrew Zisserman
2015	J	`jnl`	Tree-Cut for Probabilistic Image Segmentation. CoRR Shell Xu Hu, Christopher K. I. Williams, Sinisa Todorovic
2014	A	`conf`	A Hierarchical Switching Linear Dynamical System Applied to the Detection of Sepsis in Neonatal Condition Monitoring. UAI Ioan Stanculescu, Christopher K. I. Williams, Yvonne Freer
2014	J	`jnl`	Autoregressive Hidden Markov Models for the Early Detection of Neonatal Sepsis. IEEE J. Biomed. Health Informatics Ioan Stanculescu, Christopher K. I. Williams, Yvonne Freer
2014	J	`jnl`	Renewal Strings for Cleaning Astronomical Databases. CoRR Amos J. Storkey, Nigel C. Hambly, Christopher K. I. Williams, Robert G. Mann
2014	J	`jnl`	The Shape Boltzmann Machine: A Strong Model of Object Shape. Int. J. Comput. Vis. S. M. Ali Eslami, Nicolas Heess, Christopher K. I. Williams, John M. Winn
2014	A	`conf`	Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection. AISTATS Jyri J. Kivinen, Christopher K. I. Williams, Nicolas Heess
2013	J	`jnl`	A framework for evaluating approximation methods for Gaussian process regression. J. Mach. Learn. Res. Krzysztof Chalupka, Christopher K. I. Williams, Iain Murray
2013	—	`book`	Dictionary of Computer Vision and Image Processing, Second Edition. Robert B. Fisher, Toby P. Breckon, Kenneth M. Dawson-Howe, Andrew W. Fitzgibbon, Craig Robertson, Emanuele Trucco, Christopher K. I. Williams
2012	J	`jnl`	A Framework for Evaluating Approximation Methods for Gaussian Process Regression CoRR Krzysztof Chalupka, Christopher K. I. Williams, Iain Murray
2012	—	`conf`	A Generative Model for Parts-based Object Segmentation. NIPS S. M. Ali Eslami, Christopher K. I. Williams
2012	J	`jnl`	In Memoriam: Mark Everingham. IEEE Trans. Pattern Anal. Mach. Intell. Andrew Zisserman, John M. Winn, Andrew W. Fitzgibbon, Luc Van Gool, Josef Sivic, Christopher K. I. Williams, David C. Hogg
2012	A	`conf`	Multiple Texture Boltzmann Machines. AISTATS Jyri J. Kivinen, Christopher K. I. Williams
2011	B	`conf`	Automating the Calibration of a Neonatal Condition Monitoring System. AIME Christopher K. I. Williams, Ioan Stanculescu
2011	A	`conf`	Factored Shapes and Appearances for Parts-based Object Understanding. BMVC Seyed Mohammadali Eslami, Christopher K. I. Williams
2011	J	`jnl`	Greedy Learning of Binary Latent Trees. IEEE Trans. Pattern Anal. Mach. Intell. Stefan Harmeling, Christopher K. I. Williams
2011	J	`jnl`	Milepost GCC: Machine Learning Enabled Self-tuning Compiler. Int. J. Parallel Program. Grigori Fursin, Yuriy Kashnikov, Abdul Wahid Memon, Zbigniew Chamski, Olivier Temam, Mircea Namolaru, Elad Yom-Tov, Bilha Mendelson, Ayal Zaks, Eric Courtois, François Bodin, Phil Barnard, Elton Ashton, Edwin V. Bonilla, John Thomson, Christopher K. I. Williams, Michael F. P. O'Boyle
2011	J	`jnl`	Special Issue on Probabilistic Models for Image Understanding, Part II. Int. J. Comput. Vis. Bill Triggs, Christopher K. I. Williams
2011	—	`conf`	Transformation Equivariant Boltzmann Machines. ICANN (1) Jyri J. Kivinen, Christopher K. I. Williams
2010	—	`ed.`	Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, Vancouver, British Columbia, Canada. NIPS John D. Lafferty, Christopher K. I. Williams, John Shawe-Taylor, Richard S. Zemel, Aron Culotta
2010	J	`jnl`	Editorial: Special Issue on Probabilistic Models for Image Understanding. Int. J. Comput. Vis. Bill Triggs, Christopher K. I. Williams
2010	J	`jnl`	The Pascal Visual Object Classes (VOC) Challenge. Int. J. Comput. Vis. Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John M. Winn, Andrew Zisserman
2009	—	`ed.`	Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, Vancouver, British Columbia, Canada. NIPS Yoshua Bengio, Dale Schuurmans, John D. Lafferty, Christopher K. I. Williams, Aron Culotta
2009	J	`jnl`	Factorial Switching Linear Dynamical Systems Applied to Physiological Condition Monitoring. IEEE Trans. Pattern Anal. Mach. Intell. John A. Quinn, Christopher K. I. Williams, Neil McIntosh
2009	A	`conf`	Learning Generative Texture Models with extended Fields-of-Experts. BMVC Nicolas Heess, Christopher K. I. Williams, Geoffrey E. Hinton
2009	J	`jnl`	Object localisation using the Generative Template of Features. Comput. Vis. Image Underst. Moray Allan, Christopher K. I. Williams
2008	—	`conf`	Multi-task Gaussian Process Learning of Robot Inverse Dynamics. NIPS Kian Ming Adam Chai, Christopher K. I. Williams, Stefan Klanke, Sethu Vijayakumar
2008	Misc	`conf`	Signal masking in Gaussian channels. ICASSP John A. Quinn, Christopher K. I. Williams
2007	A	`conf`	Kernel Multi-task Learning using Task-specific Features. AISTATS Edwin V. Bonilla, Felix V. Agakov, Christopher K. I. Williams
2007	—	`conf`	Known Unknowns: Novelty Detection in Condition Monitoring. IbPRIA (1) John A. Quinn, Christopher K. I. Williams
2007	—	`conf`	Multi-task Gaussian Process Prediction. NIPS Edwin V. Bonilla, Kian Ming Adam Chai, Christopher K. I. Williams
2006	J	`jnl`	A regularized discriminative model for the prediction of protein-peptide interactions. Bioinform. Wolfgang P. Lehrach, Dirk Husmeier, Christopher K. I. Williams
2006	—	`conf`	Dataset Issues in Object Recognition. Toward Category-Level Object Recognition Jean Ponce, Tamara L. Berg, Mark Everingham, David A. Forsyth, Martial Hebert, Svetlana Lazebnik, Marcin Marszalek, Cordelia Schmid, Bryan C. Russell, Antonio Torralba, Christopher K. I. Williams, Jianguo Zhang, Andrew Zisserman
2006	—	`book`	Gaussian processes for machine learning. Carl Edward Rasmussen, Christopher K. I. Williams
2006	A*	`conf`	Predictive search distributions. ICML Edwin V. Bonilla, Christopher K. I. Williams, Felix V. Agakov, John Cavazos, John Thomson, Michael F. P. O'Boyle
2006	—	`conf`	Sequential Learning of Layered Models from Video. Toward Category-Level Object Recognition Michalis K. Titsias, Christopher K. I. Williams
2006	A	`conf`	Using Machine Learning to Focus Iterative Optimization. CGO Felix V. Agakov, Edwin V. Bonilla, John Cavazos, Björn Franke, Grigori Fursin, Michael F. P. O'Boyle, John Thomson, Marc Toussaint, Christopher K. I. Williams
2005	—	`conf`	Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care. NIPS Christopher K. I. Williams, John A. Quinn, Neil McIntosh
2005	A	`conf`	Fast Learning of Sprites using Invariant Features. BMVC Moray Allan, Michalis K. Titsias, Christopher K. I. Williams
2005	J	`jnl`	How to Pretend That Correlated Variables Are Independent by Using Difference Observations. Neural Comput. Christopher K. I. Williams
2005	J	`jnl`	On the eigenspectrum of the gram matrix and the generalization error of kernel-PCA. IEEE Trans. Inf. Theory John Shawe-Taylor, Christopher K. I. Williams, Nello Cristianini, Jaz S. Kandola
2005	—	`conf`	Probabilistic Systems Biology and Regulatory Genomics Wolfgang P. Lehrach, Dirk Husmeier, Christopher K. I. Williams
2005	—	`conf`	The 2005 PASCAL Visual Object Classes Challenge. MLCW Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc Van Gool, Moray Allan, Christopher M. Bishop, Olivier Chapelle, Navneet Dalal, Thomas Deselaers, Gyuri Dorkó, Stefan Duffner, Jan Eichhorn, Jason D. R. Farquhar, Mario Fritz, Christophe Garcia, Tom Griffiths, Frédéric Jurie, Daniel Keysers, Markus Koskela, Jorma Laaksonen, Diane Larlus, Bastian Leibe, Hongying Meng, Hermann Ney, Bernt Schiele, Cordelia Schmid, Edgar Seemann, John Shawe-Taylor, Amos J. Storkey, Sándor Szedmák, Bill Triggs, Ilkay Ulusoy, Ville Viitaniemi, Jianguo Zhang
2005	—	`conf`	Unsupervised Learning of Multiple Aspects of Moving Objects from Video. Panhellenic Conference on Informatics Michalis K. Titsias, Christopher K. I. Williams
2004	—	`conf`	Fast Unsupervised Greedy Learning of Multiple Objects and Parts from Video. CVPR Workshops Michalis K. Titsias, Christopher K. I. Williams
2004	J	`jnl`	Greedy Learning of Multiple Objects in Images Using Robust Statistics and Factorial Learning. Neural Comput. Christopher K. I. Williams, Michalis K. Titsias
2004	—	`conf`	Harmonising Chorales by Probabilistic Inference. NIPS Moray Allan, Christopher K. I. Williams
2004	—	`conf`	Understanding Gaussian Process Regression Using the Equivalent Kernel. Deterministic and Statistical Methods in Machine Learning Peter Sollich, Christopher K. I. Williams
2004	—	`conf`	Using the Equivalent Kernel to Understand Gaussian Process Regression. NIPS Peter Sollich, Christopher K. I. Williams
2003	J	`jnl`	Dynamic trees for image modelling. Image Vis. Comput. Nicholas J. Adams, Christopher K. I. Williams
2003	—	`conf`	Extreme Components Analysis. NIPS Max Welling, Felix V. Agakov, Christopher K. I. Williams
2003	A	`conf`	Fast Forward Selection to Speed Up Sparse Gaussian Process Regression. AISTATS Matthias W. Seeger, Christopher K. I. Williams, Neil D. Lawrence
2003	J	`jnl`	Image Modeling with Position-Encoding Dynamic Trees. IEEE Trans. Pattern Anal. Mach. Intell. Amos J. Storkey, Christopher K. I. Williams
2003	—	`conf`	On the Number of Modes of a Gaussian Mixture. Scale-Space Miguel Á. Carreira-Perpiñán, Christopher K. I. Williams
2003	A	`conf`	Renewal Strings for Cleaning Astronomical Databases. UAI Amos J. Storkey, Nigel C. Hambly, Christopher K. I. Williams, Robert G. Mann
2002	J	`jnl`	Combining Belief Networks and Neural Networks for Scene Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. Xiaojuan Feng, Christopher K. I. Williams, Stephen N. Felderhof
2002	—	`conf`	Dynamic Trees: Learning to Model Outdoor Scenes. ECCV (4) Nicholas J. Adams, Christopher K. I. Williams
2002	—	`conf`	Learning About Multiple Objects in Images: Factorial Learning without Factorial Search. NIPS Christopher K. I. Williams, Michalis K. Titsias
2002	J	`jnl`	On a Connection between Kernel PCA and Metric Multidimensional Scaling. Mach. Learn. Christopher K. I. Williams
2002	B	`conf`	On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum. ALT John Shawe-Taylor, Christopher K. I. Williams, Nello Cristianini, Jaz S. Kandola
2002	B	`conf`	On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum. Discovery Science John Shawe-Taylor, Christopher K. I. Williams, Nello Cristianini, Jaz S. Kandola
2002	J	`jnl`	Products of Gaussians and Probabilistic Minor Component Analysis. Neural Comput. Christopher K. I. Williams, Felix V. Agakov
2002	—	`conf`	The Stability of Kernel Principal Components Analysis and its Relation to the Process Eigenspectrum. NIPS John Shawe-Taylor, Christopher K. I. Williams
2001	J	`jnl`	Comparing Bayesian neural network algorithms for classifying segmented outdoor images. Neural Networks Francesco Vivarelli, Christopher K. I. Williams
2001	A	`conf`	Dynamic Positional Trees for Structural Image Analysis. AISTATS Amos J. Storkey, Christopher K. I. Williams
2001	—	`conf`	Products of Gaussians. NIPS Christopher K. I. Williams, Felix V. Agakov, Stephen N. Felderhof
2000	J	`jnl`	Bayesian inference for wind field retrieval. Neurocomputing Ian T. Nabney, Dan Cornford, Christopher K. I. Williams
2000	B	`conf`	MFDTs: Mean Field Dynamic Trees. ICPR Nicholas J. Adams, Amos J. Storkey, Christopher K. I. Williams, Zoubin Ghahramani
2000	—	`conf`	On a Connection between Kernel PCA and Metric Multidimensional Scaling. NIPS Christopher K. I. Williams
2000	A*	`conf`	The Effect of the Input Density Distribution on Kernel-based Classifiers. ICML Christopher K. I. Williams, Matthias W. Seeger
2000	J	`jnl`	Upper and Lower Bounds on the Learning Curve for Gaussian Processes. Mach. Learn. Christopher K. I. Williams, Francesco Vivarelli
2000	—	`conf`	Using the Nyström Method to Speed Up Kernel Machines. NIPS Christopher K. I. Williams, Matthias W. Seeger
1999	—	`conf`	A MCMC Approach to Hierarchical Mixture Modelling. NIPS Christopher K. I. Williams
1998	—	`conf`	Adding Constrained Discontinuities to Gaussian Process Models of Wind Fields. NIPS Dan Cornford, Ian T. Nabney, Christopher K. I. Williams
1998	J	`jnl`	Bayesian Classification With Gaussian Processes. IEEE Trans. Pattern Anal. Mach. Intell. Christopher K. I. Williams, David Barber
1998	J	`jnl`	Computation with Infinite Neural Networks. Neural Comput. Christopher K. I. Williams
1998	—	`conf`	DTs: Dynamic Trees. NIPS Christopher K. I. Williams, Nicholas J. Adams
1998	J	`jnl`	Developments of the generative topographic mapping. Neurocomputing Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams
1998	—	`conf`	Discovering Hidden Features with Gaussian Processes Regression. NIPS Francesco Vivarelli, Christopher K. I. Williams
1998	—	`conf`	Finite-Dimensional Approximation of Gaussian Processes. NIPS Giancarlo Ferrari-Trecate, Christopher K. I. Williams, Manfred Opper
1998	J	`jnl`	GTM: The Generative Topographic Mapping. Neural Comput. Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams
1998	—	`ch.`	Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond. Learning in Graphical Models Christopher K. I. Williams
1997	J	`jnl`	Instantiating Deformable Models with a Neural Net. Comput. Vis. Image Underst. Christopher K. I. Williams, Michael Revow, Geoffrey E. Hinton
1997	—	`conf`	Regression with Input-dependent Noise: A Gaussian Process Treatment. NIPS Paul W. Goldberg, Christopher K. I. Williams, Christopher M. Bishop
1996	—	`conf`	Computing with Infinite Networks. NIPS Christopher K. I. Williams
1996	C	`conf`	GTM: A Principled Alternative to the Self-Organizing Map. ICANN Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams
1996	—	`conf`	GTM: A Principled Alternative to the Self-Organizing Map. NIPS Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams
1996	—	`conf`	Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo. NIPS David Barber, Christopher K. I. Williams
1996	J	`jnl`	Using Generative Models for Handwritten Digit Recognition. IEEE Trans. Pattern Anal. Mach. Intell. Michael Revow, Christopher K. I. Williams, Geoffrey E. Hinton
1995	—	`conf`	EM Optimization of Latent-Variables Density Models. NIPS Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams
1995	—	`conf`	Gaussian Processes for Regression. NIPS Christopher K. I. Williams, Carl Edward Rasmussen
1995	J	`jnl`	Lending direction to neural networks. Neural Networks Richard S. Zemel, Christopher K. I. Williams, Michael Mozer
1994	—	`—`	Combining deformable models and neural networks for handprinted digit recognition. Christopher K. I. Williams
1994	—	`conf`	Using a neural net to instantiate a deformable model. NIPS Christopher K. I. Williams, Michael Revow, Geoffrey E. Hinton
1992	—	`conf`	Directional-Unit Boltzmann Machines. NIPS Richard S. Zemel, Christopher K. I. Williams, Michael Mozer
1992	J	`jnl`	Learning to Segment Images Using Dynamic Feature Binding. Neural Comput. Michael C. Mozer, Richard S. Zemel, Marlene Behrmann, Christopher K. I. Williams
1991	—	`conf`	Adaptive Elastic Models for Hand-Printed Character Recognition. NIPS Geoffrey E. Hinton, Christopher K. I. Williams, Michael Revow