---
res:
  bibo_abstract:
  - 'In multi-task learning, a learner is given a collection of prediction tasks and
    needs to solve all of them. In contrast to previous work, which required that
    annotated training data must be available for all tasks, we consider a new setting,
    in which for some tasks, potentially most of them, only unlabeled training data
    is provided. Consequently, to solve all tasks, information must be transferred
    between tasks with labels and tasks without labels. Focusing on an instance-based
    transfer method we analyze two variants of this setting: when the set of labeled
    tasks is fixed, and when it can be actively selected by the learner. We state
    and prove a generalization bound that covers both scenarios and derive from it
    an algorithm for making the choice of labeled tasks (in the active case) and for
    transferring information between the tasks in a principled way. We also illustrate
    the effectiveness of the algorithm on synthetic and real data. @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: 70
  dct_date: 2017^xs_gYear
  dct_identifier:
  - UT:000683309502093
  dct_isPartOf:
  - http://id.crossref.org/issn/9781510855144
  dct_language: eng
  dct_publisher: ML Research Press@
  dct_title: Multi-task learning with labeled and unlabeled tasks@
...