Adaptive Elastic Models for Hand-Printed Character Recognition. - Geoffrey E. Hinton; Christopher K. I. Williams; Michael Revow (NIPS, 1991)

Directional-Unit Boltzmann Machines. - Richard S. Zemel; Christopher K. I. Williams; Michael Mozer (NIPS, 1992)

Learning to Segment Images Using Dynamic Feature Binding. - Michael C. Mozer; Richard S. Zemel; Marlene Behrmann; Christopher K. I. Williams (Neural Comput., 1992)

Using a neural net to instantiate a deformable model. - Christopher K. I. Williams; Michael Revow; Geoffrey E. Hinton (NIPS, 1994)

Combining deformable models and neural networks for handprinted digit recognition. - Christopher K. I. Williams (1994)

EM Optimization of Latent-Variables Density Models. - Christopher M. Bishop; Markus Svensén; Christopher K. I. Williams (NIPS, 1995)

Gaussian Processes for Regression. - Christopher K. I. Williams; Carl Edward Rasmussen (NIPS, 1995)

Lending direction to neural networks. - Richard S. Zemel; Christopher K. I. Williams; Michael Mozer (Neural Networks, 1995)

Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo. - David Barber; Christopher K. I. Williams (NIPS, 1996)

GTM: A Principled Alternative to the Self-Organizing Map. - Christopher M. Bishop; Markus Svensén; Christopher K. I. Williams (NIPS, 1996)

Computing with Infinite Networks. - Christopher K. I. Williams (NIPS, 1996)

Using Generative Models for Handwritten Digit Recognition. - Michael Revow; Christopher K. I. Williams; Geoffrey E. Hinton (IEEE Trans. Pattern Anal. Mach. Intell., 1996)

Regression with Input-dependent Noise: A Gaussian Process Treatment. - Paul W. Goldberg; Christopher K. I. Williams; Christopher M. Bishop (NIPS, 1997)

Instantiating Deformable Models with a Neural Net. - Christopher K. I. Williams; Michael Revow; Geoffrey E. Hinton (Comput. Vis. Image Underst., 1997)

Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond. - Christopher K. I. Williams (Learning in Graphical Models, 1998)

Adding Constrained Discontinuities to Gaussian Process Models of Wind Fields. - Dan Cornford; Ian T. Nabney; Christopher K. I. Williams (NIPS, 1998)

Finite-Dimensional Approximation of Gaussian Processes. - Giancarlo Ferrari-Trecate; Christopher K. I. Williams; Manfred Opper (NIPS, 1998)

Discovering Hidden Features with Gaussian Processes Regression. - Francesco Vivarelli; Christopher K. I. Williams (NIPS, 1998)

DTs: Dynamic Trees. - Christopher K. I. Williams; Nicholas J. Adams (NIPS, 1998)

Developments of the generative topographic mapping. - Christopher M. Bishop; Markus Svensén; Christopher K. I. Williams (Neurocomputing, 1998)

GTM: The Generative Topographic Mapping. - Christopher M. Bishop; Markus Svensén; Christopher K. I. Williams (Neural Comput., 1998)

Computation with Infinite Neural Networks. - Christopher K. I. Williams (Neural Comput., 1998)

Bayesian Classification With Gaussian Processes. - Christopher K. I. Williams; David Barber (IEEE Trans. Pattern Anal. Mach. Intell., 1998)

A MCMC Approach to Hierarchical Mixture Modelling. - Christopher K. I. Williams (NIPS, 1999)

The Effect of the Input Density Distribution on Kernel-based Classifiers. - Christopher K. I. Williams; Matthias W. Seeger (ICML, 2000)

MFDTs: Mean Field Dynamic Trees. - Nicholas J. Adams; Amos J. Storkey; Christopher K. I. Williams; Zoubin Ghahramani (ICPR, 2000)

Using the Nyström Method to Speed Up Kernel Machines. - Christopher K. I. Williams; Matthias W. Seeger (NIPS, 2000)

Bayesian inference for wind field retrieval. - Ian T. Nabney; Dan Cornford; Christopher K. I. Williams (Neurocomputing, 2000)

Upper and Lower Bounds on the Learning Curve for Gaussian Processes. - Christopher K. I. Williams; Francesco Vivarelli (Mach. Learn., 2000)

Dynamic Positional Trees for Structural Image Analysis. - Amos J. Storkey; Christopher K. I. Williams (AISTATS, 2001)

Products of Gaussians. - Christopher K. I. Williams; Felix V. Agakov; Stephen N. Felderhof (NIPS, 2001)

Comparing Bayesian neural network algorithms for classifying segmented outdoor images. - Francesco Vivarelli; Christopher K. I. Williams (Neural Networks, 2001)

On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum. - John Shawe-Taylor; Christopher K. I. Williams; Nello Cristianini; Jaz S. Kandola (ALT, 2002)

Dynamic Trees: Learning to Model Outdoor Scenes. - Nicholas J. Adams; Christopher K. I. Williams (ECCV (4), 2002)

The Stability of Kernel Principal Components Analysis and its Relation to the Process Eigenspectrum. - John Shawe-Taylor; Christopher K. I. Williams (NIPS, 2002)

Learning About Multiple Objects in Images: Factorial Learning without Factorial Search. - Christopher K. I. Williams; Michalis K. Titsias (NIPS, 2002)

On a Connection between Kernel PCA and Metric Multidimensional Scaling. - Christopher K. I. Williams (Mach. Learn., 2002)

Products of Gaussians and Probabilistic Minor Component Analysis. - Christopher K. I. Williams; Felix V. Agakov (Neural Comput., 2002)

Combining Belief Networks and Neural Networks for Scene Segmentation. - Xiaojuan Feng; Christopher K. I. Williams; Stephen N. Felderhof (IEEE Trans. Pattern Anal. Mach. Intell., 2002)

Fast Forward Selection to Speed Up Sparse Gaussian Process Regression. - Matthias W. Seeger; Christopher K. I. Williams; Neil D. Lawrence (AISTATS, 2003)

Extreme Components Analysis. - Max Welling; Felix V. Agakov; Christopher K. I. Williams (NIPS, 2003)

On the Number of Modes of a Gaussian Mixture. - Miguel Á. Carreira-Perpiñán; Christopher K. I. Williams (Scale-Space, 2003)

Renewal Strings for Cleaning Astronomical Databases. - Amos J. Storkey; Nigel C. Hambly; Christopher K. I. Williams; Robert G. Mann (UAI, 2003)

Dynamic trees for image modelling. - Nicholas J. Adams; Christopher K. I. Williams (Image Vis. Comput., 2003)

Image Modeling with Position-Encoding Dynamic Trees. - Amos J. Storkey; Christopher K. I. Williams (IEEE Trans. Pattern Anal. Mach. Intell., 2003)

Fast Unsupervised Greedy Learning of Multiple Objects and Parts from Video. - Michalis K. Titsias; Christopher K. I. Williams (CVPR Workshops, 2004)

Understanding Gaussian Process Regression Using the Equivalent Kernel. - Peter Sollich; Christopher K. I. Williams (Deterministic and Statistical Methods in Machine Learning, 2004)

Harmonising Chorales by Probabilistic Inference. - Moray Allan; Christopher K. I. Williams (NIPS, 2004)

Using the Equivalent Kernel to Understand Gaussian Process Regression. - Peter Sollich; Christopher K. I. Williams (NIPS, 2004)

Greedy Learning of Multiple Objects in Images Using Robust Statistics and Factorial Learning. - Christopher K. I. Williams; Michalis K. Titsias (Neural Comput., 2004)

Fast Learning of Sprites using Invariant Features. - Moray Allan; Michalis K. Titsias; Christopher K. I. Williams (BMVC, 2005)

The 2005 PASCAL Visual Object Classes Challenge. - 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; et al. (MLCW, 2005)

Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care. - Christopher K. I. Williams; John A. Quinn; Neil McIntosh (NIPS, 2005)

Unsupervised Learning of Multiple Aspects of Moving Objects from Video. - Michalis K. Titsias; Christopher K. I. Williams (Panhellenic Conference on Informatics, 2005)

Probabilistic in Silico Prediction of Protein-Peptide Interactions. - Wolfgang P. Lehrach; Dirk Husmeier; Christopher K. I. Williams (Systems Biology and Regulatory Genomics, 2005)

How to Pretend That Correlated Variables Are Independent by Using Difference Observations. - Christopher K. I. Williams (Neural Comput., 2005)

On the eigenspectrum of the gram matrix and the generalization error of kernel-PCA. - John Shawe-Taylor; Christopher K. I. Williams; Nello Cristianini; Jaz S. Kandola (IEEE Trans. Inf. Theory, 2005)

Gaussian processes for machine learning. - Carl Edward Rasmussen; Christopher K. I. Williams (MIT Press, 2006)

Using Machine Learning to Focus Iterative Optimization. - 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 (CGO, 2006)

Dataset Issues in 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 001; Christopher K. I. Williams; Jianguo Zhang 001; et al. (Toward Category-Level Object Recognition, 2006)

Sequential Learning of Layered Models from Video. - Michalis K. Titsias; Christopher K. I. Williams (Toward Category-Level Object Recognition, 2006)

Predictive search distributions. - Edwin V. Bonilla; Christopher K. I. Williams; Felix V. Agakov; John Cavazos; John Thomson; Michael F. P. O'Boyle (ICML, 2006)

A regularized discriminative model for the prediction of protein-peptide interactions. - Wolfgang P. Lehrach; Dirk Husmeier; Christopher K. I. Williams (Bioinform., 2006)

Known Unknowns: Novelty Detection in Condition Monitoring. - John A. Quinn; Christopher K. I. Williams (IbPRIA (1), 2007)

Multi-task Gaussian Process Prediction. - Edwin V. Bonilla; Kian Ming Adam Chai; Christopher K. I. Williams (NIPS, 2007)

Kernel Multi-task Learning using Task-specific Features. - Edwin V. Bonilla; Felix V. Agakov; Christopher K. I. Williams (AISTATS, 2007)

Signal masking in Gaussian channels. - John A. Quinn; Christopher K. I. Williams (ICASSP, 2008)

Multi-task Gaussian Process Learning of Robot Inverse Dynamics. - Kian Ming Adam Chai; Christopher K. I. Williams; Stefan Klanke; Sethu Vijayakumar (NIPS, 2008)

Learning Generative Texture Models with extended Fields-of-Experts. - Nicolas Heess; Christopher K. I. Williams; Geoffrey E. Hinton (BMVC, 2009)

Object localisation using the Generative Template of Features. - Moray Allan; Christopher K. I. Williams (Comput. Vis. Image Underst., 2009)

Factorial Switching Linear Dynamical Systems Applied to Physiological Condition Monitoring. - John A. Quinn; Christopher K. I. Williams; Neil McIntosh (IEEE Trans. Pattern Anal. Mach. Intell., 2009)

The Pascal Visual Object Classes (VOC) Challenge. - Mark Everingham; Luc Van Gool; Christopher K. I. Williams; John M. Winn; Andrew Zisserman (Int. J. Comput. Vis., 2010)

Editorial: Special Issue on Probabilistic Models for Image Understanding. - Bill Triggs; Christopher K. I. Williams (Int. J. Comput. Vis., 2010)

Automating the Calibration of a Neonatal Condition Monitoring System. - Christopher K. I. Williams; Ioan Stanculescu (AIME, 2011)

Factored Shapes and Appearances for Parts-based Object Understanding. - Seyed Mohammadali Eslami; Christopher K. I. Williams (BMVC, 2011)

Transformation Equivariant Boltzmann Machines. - Jyri J. Kivinen; Christopher K. I. Williams (ICANN (1), 2011)

Special Issue on Probabilistic Models for Image Understanding, Part II. - Bill Triggs; Christopher K. I. Williams (Int. J. Comput. Vis., 2011)

Milepost GCC: Machine Learning Enabled Self-tuning Compiler. - 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; et al. (Int. J. Parallel Program., 2011)

Greedy Learning of Binary Latent Trees. - Stefan Harmeling; Christopher K. I. Williams (IEEE Trans. Pattern Anal. Mach. Intell., 2011)

A Generative Model for Parts-based Object Segmentation. - S. M. Ali Eslami; Christopher K. I. Williams (NIPS, 2012)

Multiple Texture Boltzmann Machines. - Jyri J. Kivinen; Christopher K. I. Williams (AISTATS, 2012)

In Memoriam: Mark Everingham. - Andrew Zisserman; John M. Winn; Andrew W. Fitzgibbon; Luc Van Gool; Josef Sivic; Christopher K. I. Williams; David C. Hogg (IEEE Trans. Pattern Anal. Mach. Intell., 2012)

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 (Wiley, 2013)

A framework for evaluating approximation methods for Gaussian process regression. - Krzysztof Chalupka; Christopher K. I. Williams; Iain Murray 001 (J. Mach. Learn. Res., 2013)

Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection. - Jyri J. Kivinen; Christopher K. I. Williams; Nicolas Heess (AISTATS, 2014)

A Hierarchical Switching Linear Dynamical System Applied to the Detection of Sepsis in Neonatal Condition Monitoring. - Ioan Stanculescu; Christopher K. I. Williams; Yvonne Freer (UAI, 2014)

The Shape Boltzmann Machine: A Strong Model of Object Shape. - S. M. Ali Eslami; Nicolas Heess; Christopher K. I. Williams; John M. Winn (Int. J. Comput. Vis., 2014)

Autoregressive Hidden Markov Models for the Early Detection of Neonatal Sepsis. - Ioan Stanculescu; Christopher K. I. Williams; Yvonne Freer (IEEE J. Biomed. Health Informatics, 2014)

Discriminative Switching Linear Dynamical Systems applied to Physiological Condition Monitoring. - Konstantinos Georgatzis; Christopher K. I. Williams (UAI, 2015)

Tree-Cut for Probabilistic Image Segmentation. - Shell Xu Hu; Christopher K. I. Williams; Sinisa Todorovic (CoRR, 2015)

The Pascal Visual Object Classes Challenge: A Retrospective. - Mark Everingham; S. M. Ali Eslami; Luc Van Gool; Christopher K. I. Williams; John M. Winn; Andrew Zisserman (Int. J. Comput. Vis., 2015)

Overcoming Occlusion with Inverse Graphics. - Pol Moreno; Christopher K. I. Williams; Charlie Nash; Pushmeet Kohli (ECCV Workshops (3), 2016)

Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring. - Konstantinos Georgatzis; Christopher K. I. Williams; Christopher Hawthorne (MLHC, 2016)

Predicting Patient State-of-Health using Sliding Window and Recurrent Classifiers. - Adam McCarthy; Christopher K. I. Williams (CoRR, 2016)

Vision-as-Inverse-Graphics: Obtaining a Rich 3D Explanation of a Scene from a Single Image. - Lukasz Romaszko; Christopher K. I. Williams; Pol Moreno; Pushmeet Kohli (ICCV Workshops, 2017)

Estimating Bacterial Load in FCFM Imaging. - Sohan Seth; Ahsan R. Akram; Kevin Dhaliwal; Christopher K. I. Williams (MIUA, 2017)

The shape variational autoencoder: A deep generative model of part-segmented 3D objects. - Charlie Nash; Christopher K. I. Williams (Comput. Graph. Forum, 2017)

A Framework for the Quantitative Evaluation of Disentangled Representations. - Cian Eastwood; Christopher K. I. Williams (ICLR (Poster), 2018)

Autoencoders and Probabilistic Inference with Missing Data: An Exact Solution for The Factor Analysis Case. - Christopher K. I. Williams; Charlie Nash (CoRR, 2018)

Estimating Bacterial and Cellular Load in FCFM Imaging. - Sohan Seth; Ahsan R. Akram; Kevin Dhaliwal; Christopher K. I. Williams (J. Imaging, 2018)

Multi-Task Time Series Analysis applied to Drug Response Modelling. - Alex Bird; Christopher K. I. Williams; Christopher Hawthorne (AISTATS, 2019)

Inverting Supervised Representations with Autoregressive Neural Density Models. - Charlie Nash; Nate Kushman; Christopher K. I. Williams (AISTATS, 2019)

The Extended Dawid-Skene Model: Fusing Information from Multiple Data Schemas. - Michael P. J. Camilleri; Christopher K. I. Williams (CoRR, 2019)

Robust Variational Autoencoders for Outlier Detection in Mixed-Type Data. - Simão Eduardo; Alfredo Nazábal; Christopher K. I. Williams; Charles Sutton (CoRR, 2019)

Customizing Sequence Generation with Multi-Task Dynamical Systems. - Alex Bird; Christopher K. I. Williams (CoRR, 2019)

Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data. - Simão Eduardo; Alfredo Nazábal; Christopher K. I. Williams; Charles Sutton (AISTATS, 2020)

An Evaluation of Change Point Detection Algorithms. - Gerrit J. J. van den Burg; Christopher K. I. Williams (CoRR, 2020)

Data Engineering for Data Analytics: A Classification of the Issues, and Case Studies. - Alfredo Nazábal; Christopher K. I. Williams; Giovanni Colavizza; Camila Rangel Smith; Angus Williams (CoRR, 2020)

VAEs in the Presence of Missing Data. - Mark Collier; Alfredo Nazábal; Christopher K. I. Williams (CoRR, 2020)

ptype: probabilistic type inference. - Taha Ceritli; Christopher K. I. Williams; James Geddes (Data Min. Knowl. Discov., 2020)

Learning Direct Optimization for scene understanding. - Lukasz Romaszko; Christopher K. I. Williams; John M. Winn (Pattern Recognit., 2020)

Unit-level surprise in neural networks. - Cian Eastwood; Ian Mason; Christopher K. I. Williams (ICBINB@NeurIPS, 2021)

On Memorization in Probabilistic Deep Generative Models. - Gerrit J. J. van den Burg; Christopher K. I. Williams (NeurIPS, 2021)

Inference for Generative Capsule Models. - Alfredo Nazábal; Christopher K. I. Williams (CoRR, 2021)

Automating Data Science: Prospects and Challenges. - Tijl De Bie; Luc De Raedt; José Hernández-Orallo; Holger H. Hoos; Padhraic Smyth; Christopher K. I. Williams (CoRR, 2021)

ptype-cat: Inferring the Type and Values of Categorical Variables. - Taha Ceritli; Christopher K. I. Williams (CoRR, 2021)

Identifying the Units of Measurement in Tabular Data. - Taha Ceritli; Christopher K. I. Williams (CoRR, 2021)

Tracking and Long-Term Identification Using Non-Visual Markers. - Michael P. J. Camilleri; Li Zhang; Rasneer S. Bains; Andrew Zisserman; Christopher K. I. Williams (CoRR, 2021)

The Effect of Class Imbalance on Precision-Recall Curves. - Christopher K. I. Williams (Neural Comput., 2021)

Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration. - Cian Eastwood; Ian Mason; Christopher K. I. Williams; Bernhard Schölkopf (ICLR, 2022)

Automating data science. - Tijl De Bie; Luc De Raedt; José Hernández-Orallo; Holger H. Hoos; Padhraic Smyth; Christopher K. I. Williams (Commun. ACM, 2022)

Align-Deform-Subtract: An Interventional Framework for Explaining Object Differences. - Cian Eastwood; Nanbo Li; Christopher K. I. Williams (CoRR, 2022)

The Elliptical Quartic Exponential Distribution: An Annular Distribution Obtained via Maximum Entropy. - Christopher K. I. Williams (CoRR, 2022)

Multi-Task Dynamical Systems. - Alex Bird; Christopher K. I. Williams; Christopher Hawthorne (J. Mach. Learn. Res., 2022)

On Suspicious Coincidences and Pointwise Mutual Information. - Christopher K. I. Williams (Neural Comput., 2022)

The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence. - Hector Zenil; Jesper Tegnér; Felipe S. Abrahão; Alexander Lavin; Vipin Kumar 001; Jeremy G. Frey; Adrian Weller; Larisa N. Soldatova; Alan R. Bundy; Nicholas R. Jennings; Koichi Takahashi; Lawrence Hunter; et al. (CoRR, 2023)

Inference and Learning for Generative Capsule Models. - Alfredo Nazábal; Nikolaos Tsagkas; Christopher K. I. Williams (Neural Comput., 2023)

Persistent animal identification leveraging non-visual markers. - Michael P. J. Camilleri; Li Zhang; Rasneer S. Bains; Andrew Zisserman; Christopher K. I. Williams (Mach. Vis. Appl., 2023)

AI Assistants: A Framework for Semi-Automated Data Wrangling. - Tomas Petricek 001; Gerrit J. J. van den Burg; Alfredo Nazábal; Taha Ceritli; Ernesto Jiménez-Ruiz; Christopher K. I. Williams (IEEE Trans. Knowl. Data Eng., 2023)

Naive Bayes Classifiers and One-hot Encoding of Categorical Variables. - Christopher K. I. Williams (CoRR, 2024)

Of Mice and Mates: Automated Classification and Modelling of Mouse Behaviour in Groups Using a Single Model Across Cages. - Michael P. J. Camilleri; Rasneer S. Bains; Christopher K. I. Williams (Int. J. Comput. Vis., 2024)

Fusing Foveal Fixations Using Linear Retinal Transformations and Bayesian Experimental Design. - Christopher K. I. Williams (CoRR, 2025)

Structured Generative Models for Scene Understanding. - Christopher K. I. Williams (Int. J. Comput. Vis., 2025)