---
res:
  bibo_abstract:
  - Transfer learning has received a lot of attention in the machine learning community
    over the last years, and several effective algorithms have been developed. However,
    relatively little is known about their theoretical properties, especially in the
    setting of lifelong learning, where the goal is to transfer information to tasks
    for which no data have been observed so far. In this work we study lifelong learning
    from a theoretical perspective. Our main result is a PAC-Bayesian generalization
    bound that offers a unified view on existing paradigms for transfer learning,
    such as the transfer of parameters or the transfer of low-dimensional representations.
    We also use the bound to derive two principled lifelong learning algorithms, and
    we show that these yield results comparable with existing methods.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Anastasia
      foaf_name: Pentina, Anastasia
      foaf_surname: Pentina
      foaf_workInfoHomepage: http://www.librecat.org/personId=42E87FC6-F248-11E8-B48F-1D18A9856A87
  - foaf_Person:
      foaf_givenName: Christoph
      foaf_name: Lampert, Christoph
      foaf_surname: Lampert
      foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-8622-7887
  bibo_volume: 32
  dct_date: 2014^xs_gYear
  dct_language: eng
  dct_publisher: ML Research Press@
  dct_title: A PAC-Bayesian bound for Lifelong Learning@
...