Ryan Prescott Adams, Zoubin Ghahramani and Michael I. Jordan.
Tree-Structured Stick Breaking for Hierarchical Data.
In Advances in Neural Information Processing Systems 23. 2010.
abstract | pdf | ps | bibtex | code | slides | video

Iain Murray and Ryan Prescott Adams.
Slice Sampling Covariance Hyperparameters of Latent Gaussian Models.
In Advances in Neural Information Processing Systems 23. 2010.
abstract | pdf | ps | bibtex | code

Ryan Prescott Adams, George E. Dahl and Iain Murray.
Incorporating Side Information into Probabilistic Matrix Factorization Using Gaussian Processes.
In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence. 2010.
abstract | pdf | ps | bibtex | code | slides

Ryan Prescott Adams, Hanna M. Wallach and Zoubin Ghahramani.
Learning the Structure of Deep Sparse Graphical Models.
In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics. 2010.
Winner of Best Paper Award
abstract | pdf | ps | bibtex | Hanna's slides | video

Iain Murray, Ryan Prescott Adams, and David J.C. MacKay.
Elliptical Slice Sampling.
In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics. 2010.
abstract | pdf | ps | bibtex | code

Ryan Prescott Adams and Zoubin Ghahramani.
Archipelago: Nonparametric Bayesian Semi-Supervised Learning.
In Proceedings of the 26th International Conference on Machine Learning (ICML 2009). 2009.
Honourable Mention for ICML Best Paper
abstract | pdf | ps | bibtex | slides | video

Ryan Prescott Adams, Iain Murray and David J.C. MacKay.
Tractable Nonparametric Bayesian Inference in Poisson Processes with Gaussian Process Intensities.
In Proceedings of the 26th International Conference on Machine Learning (ICML 2009). 2009.
Honourable Mention for ICML Best Student Paper
abstract | pdf | ps | bibtex | slides | video

Ryan Prescott Adams, Iain Murray and David J.C. MacKay.
The Gaussian Process Density Sampler.
In Advances in Neural Information Processing Systems 21 (NIPS 2008). 2009.
abstract | pdf | ps | bibtex | slides

Ryan Prescott Adams and Oliver Stegle.
Gaussian Process Product Models for Nonparametric Nonstationarity.
In Proceedings of the 25th International Conference on Machine Learning (ICML-2008). 2008.
abstract | pdf | ps | bibtex | Oliver's slides | video

Iain Murray and Ryan Prescott Adams.
Discussion of the paper: Riemann manifold Langevin and Hamiltonian Monte Carlo methods by Girolami and Calderhead
To appear in Journal of the Royal Statistical Society, Series B: Statistical Methodology. 2011.
pdf | ps

Ryan Prescott Adams and Richard Zemel.
Ranking via Sinkhorn Propagation.
arxiv:1106.1925v1 [stat.ML].

Jasper Snoek, Ryan Prescott Adams and Hugo Larochelle.
Semiparametric Latent Variable Models for Guided Representation.
arXiv:1102.1492 [stat.ML].

Ryan Prescott Adams, Iain Murray and David J.C. MacKay.
Nonparametric Bayesian Density Estimation with Gaussian Processes.
pdf arXiv:0912.4896v1 [stat.CO].

Jasper Snoek, Ryan Prescott Adams and Hugo Larochelle.
Semiparametric Latent Variable Models for Guided Representation.
Learning Workshop. 2011

Iain Murray and Ryan Prescott Adams.
Easy, Effective Monte Carlo Methods for Exploring Latent Gaussian Models.
Workshop on Bayesian Inference in Latent Gaussian Models. 2011.

Ryan Prescott Adams, Zoubin Ghahramani and Michael I. Jordan.
Tree-Structured Stick Breaking Processes for Hierarchical Data.
MCMSki III. 2011.

Iain Murray and Ryan Prescott Adams.
Slice Sampling with Latent Gaussian Models.
International Society for Bayesian Analysis World Meeting. 2010.

Ryan Prescott Adams, Zoubin Ghahramani and Michael I. Jordan.
Tree-Structured Stick Breaking Processes for Hierarchical Modeling.
NIPS Nonparametric Bayes Workshop. 2009.

Ryan Prescott Adams, Iain Murray and David J.C. MacKay.
Nonparametric Bayesian Density Modeling with Gaussian Processes.
ICML/UAI Nonparametric Bayes Workshop. 2008.
pdf | ps

Ryan Prescott Adams, Iain Murray and David J.C. MacKay.
The Gaussian process density sampler.
Snowbird Learning Workshop. 2008.
pdf | ps

Jeroen C. Chua, Inmar E. Givoni, Ryan Prescott Adams and Brendan J. Frey.
Bayesian Painting by Numbers: Flexible Priors for Colour-Invariant Object Recognition.
Probabilistic and Statistical Inference Group, University of Toronto Technical Report PSI TR 2011-001.
pdf | ps | bibtex

Ryan Prescott Adams and David J.C. MacKay.
Bayesian online changepoint detection.
University of Cambridge Technical Report. arXiv:0710.3742v1 [stat.ML]. 2007.
abstract | pdf | ps | bibtex

Ryan Prescott Adams
Kernel Methods for Nonparametric Bayesian Inference of Probabilities and Point Processes.
PhD Thesis, Department of Physics, University of Cambridge. 2009.
abstract | pdf | ps | bibtex