@inproceedings{3959,
  title = {A Choice Model with Infinitely Many Latent Features},
  journal = {Proceedings of the 23rd International Conference on Machine Learning (ICML 2006)},
  booktitle = {ICML 2006},
  abstract = {Elimination by aspects (EBA) is a probabilistic
  choice model describing how humans decide between several options.
  The options from which the choice is made are characterized by
  binary features and associated weights. For instance, when choosing
  which mobile phone to buy the features to consider may be: long
  lasting battery, color screen, etc. Existing methods for inferring
  the parameters of the model assume pre-specified features. However,
  the features that lead to the observed choices are not always known.
  Here, we present a non-parametric Bayesian model to infer the
  features of the options and the corresponding weights from choice
  data. We use the Indian buffet process (IBP) as a prior over the
  features. Inference using Markov chain Monte Carlo (MCMC) in
  conjugate IBP models has been previously described. The main
  contribution of this paper is an MCMC algorithm for the EBA model
  that can also be used in inference for other non-conjugate IBP
  models---this may broaden the use of IBP priors considerably.},
  pages = {361-368},
  editors = {Cohen, W. W., A. Moore},
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = jun,
  year = {2006},
  slug = {3959},
  author = {G{\"o}r{\"u}r, D. and J{\"a}kel, F. and Rasmussen, CE.},
  month_numeric = {6}
}