Edward Snelson

Bayesian inference for Plackett-Luce ranking models

This paper gives an efficient Bayesian method for inferring the parameters of a Plackett-Luce ranking model. Such models are parameterised distributions over rankings of a finite set of objects, and have typically been studied and applied within the psychometric, sociometric and econometric literature. The inference scheme is an application of Power EP (expectation propagation). The scheme is robust and can be readily applied to large scale data sets. The inference algorithm extends to variations of the basic Plackett-Luce model, including partial rankings. We show a number of advantages of the EP approach over the traditional maximum likelihood method. We apply the method to aggregate rankings of NASCAR racing drivers over the 2002 season, and also to rankings of movie genres.

Information retrieval system

A system and method are described for the access and retrieval of information, which integrates television, video and/or similar sources with the information resources available on the Internet. This invention permits a user to select an item displayed on a television screen and, without significant interruption, order the item or request additional information on the item or provide feedback to the television source signal provider, for example, the television network or advertiser.

Compact approximations to Bayesian predictive distributions

We provide a general framework for learning precise, compact, and fast representations of the Bayesian predictive distribution for a model. This framework is based on minimizing the KL divergence between the true predictive density and a suitable compact approximation. We consider various methods for doing this, both sampling based approximations, and deterministic approximations such as expectation propagation. These methods are tested on a mixture of Gaussians model for density estimation and on binary linear classification, with both synthetic data sets for visualization and several real data sets. Our results show significant reductions in prediction time and memory footprint.

A pragmatic bayesian approach to predictive uncertainty

We describe an approach to regression based on building a probabilistic model with the aid of visualization. The “stereopsis” data set in the predictive uncertainty challenge is used as a case study, for which we constructed a mixture of neural network experts model. We describe both the ideal Bayesian approach and computational shortcuts required to obtain timely results.