Ed Snelson’s Publications

PhD thesis

My thesis consists of the work on Gaussian processes done for some of the publications listed below, as well as some additional material, both theoretical and experimental.

• Edward Snelson. (2007). Flexible and efficient Gaussian process models for machine learning. PhD thesis, Gatsby Computational Neuroscience Unit, University College London.

Publications

• John Guiver and Edward Snelson. (2009). Bayesian inference for Plackett-Luce ranking models. International Conference on Machine Learning 26 (ICML).
• John Guiver and Edward Snelson. (2008). Learning to rank with SoftRank and Gaussian processes. Special Interest Group on Information Retrieval 31 (SIGIR).
• Edward Snelson and Zoubin Ghahramani. (2007). Local and global sparse Gaussian process approximations. Artificial Intelligence and Statistics 11 (AISTATS).
• Edward Snelson and Zoubin Ghahramani. (2006). Variable noise and dimensionality reduction for sparse Gaussian processes. Uncertainty in Artifical Intelligence 22 (UAI).
• Edward Snelson and Zoubin Ghahramani. (2006). Sparse Gaussian Processes using Pseudo-inputs. Neural Information Processing Systems 18 (NIPS).
• Edward Snelson and Zoubin Ghahramani. (2005). Compact Approximations to Bayesian Predictive Distributions. Proceedings of the 22^nd International Conference on Machine Learning (ICML).
• Edward Snelson, Carl Edward Rasmussen and Zoubin Ghahramani. (2004). Warped Gaussian processes. Neural Information Processing Systems 16 (NIPS).

Workshop papers

• Onno Zoeter, Michael Taylor, Edward Snelson, John Guiver, Nick Craswell, Martin Szummer. (2008). A Decision Theoretic Framework for Ranking using Implicit Feedback. Learning to Rank for Information Retrieval, SIGIR 2008 Workshop.
• Edward Snelson and John Guiver. (2007). SoftRank with Gaussian processes. Machine Learning for Web Search, NIPS 2007 Workshop.
• Onno Zoeter, Nick Craswell, Michael Taylor, John Guiver, Edward Snelson. (2007). A Decision Theoretic Framework for Implicit Relevance Feedback. Machine Learning for Web Search, NIPS 2007 Workshop.
• Iain Murray and Edward Snelson. (2006). A pragmatic Bayesian approach to predictive uncertainty. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Textual Entailment; Selected Proceedings of the First PASCAL Machine Learning Challenges Workshop. Springer LNCS.

Technical reports

• Joaquin Quiñonero Candela, Edward Snelson and Oliver Williams. (2007). Sensible Priors for Sparse Bayesian Learning. MSR-TR-2007-121.

Notes

• Edward Snelson and Zoubin Ghahramani. (2005). Variational Bayes for predictive distributions.