---
res:
  bibo_abstract:
  - In many applications, it is desirable to extract only the relevant aspects of
    data. A principled way to do this is the information bottleneck (IB) method, where
    one seeks a code that maximises information about a relevance variable, Y, while
    constraining the information encoded about the original data, X. Unfortunately
    however, the IB method is computationally demanding when data are high-dimensional
    and/or non-gaussian. Here we propose an approximate variational scheme for maximising
    a lower bound on the IB objective, analogous to variational EM. Using this method,
    we derive an IB algorithm to recover features that are both relevant and sparse.
    Finally, we demonstrate how kernelised versions of the algorithm can be used to
    address a broad range of problems with non-linear relation between X and Y.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Matthew J
      foaf_name: Chalk, Matthew J
      foaf_surname: Chalk
      foaf_workInfoHomepage: http://www.librecat.org/personId=2BAAC544-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-7782-4436
  - foaf_Person:
      foaf_givenName: Olivier
      foaf_name: Marre, Olivier
      foaf_surname: Marre
  - foaf_Person:
      foaf_givenName: Gasper
      foaf_name: Tkacik, Gasper
      foaf_surname: Tkacik
      foaf_workInfoHomepage: http://www.librecat.org/personId=3D494DCA-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0002-6699-1455
  bibo_volume: 29
  dct_date: 2016^xs_gYear
  dct_language: eng
  dct_publisher: Neural Information Processing Systems Foundation@
  dct_title: Relevant sparse codes with variational information bottleneck@
...