Mixing Rates for the Alternating Gibbs Sampler over Restricted Boltzmann Machines and Friends

@InProceedings{pmlr-v48-tosh16,
  title = 	 {Mixing Rates for the Alternating Gibbs Sampler over Restricted Boltzmann Machines and Friends},
  author = 	 {Tosh, Christopher},
  booktitle = 	 {Proceedings of The 33rd International Conference on Machine Learning},
  pages = 	 {840--849},
  year = 	 {2016},
  editor = 	 {Balcan, Maria Florina and Weinberger, Kilian Q.},
  volume = 	 {48},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {New York, New York, USA},
  month = 	 {20--22 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v48/tosh16.pdf},
  url = 	 {https://proceedings.mlr.press/v48/tosh16.html},
  abstract = 	 {Alternating Gibbs sampling is a modification of classical Gibbs sampling where several variables are simultaneously sampled from their joint conditional distribution. In this work, we investigate the mixing rate of alternating Gibbs sampling with a particular emphasis on Restricted Boltzmann Machines (RBMs) and variants.}
}