Search Machine Learning Repository: @inproceedings{icml2014c2_rey14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Sparse meta-Gaussian information bottleneck},
    Url = {http://jmlr.org/proceedings/papers/v32/rey14.pdf},
    Abstract = {We present a new sparse compression technique based on the information bottleneck (IB) principle, which takes into account side information. This is achieved by introducing a sparse variant of IB which preserves the information in only a few selected dimensions of the original data through compression. By assuming a Gaussian copula we can capture arbitrary non-Gaussian margins, continuous or discrete. We apply our model to select a sparse number of biomarkers relevant to the evolution of malignant melanoma and show that our sparse selection provides reliable predictors.},
    Author = {Melani Rey and Volker Roth and Thomas Fuchs},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {910-918}
   }