---
_id: '999'
abstract:
- lang: eng
  text: '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. '
alternative_title:
- PMLR
article_processing_charge: No
author:
- first_name: Anastasia
  full_name: Pentina, Anastasia
  id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
  last_name: Pentina
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
citation:
  ama: 'Pentina A, Lampert C. Multi-task learning with labeled and unlabeled tasks.
    In: Vol 70. ML Research Press; 2017:2807-2816.'
  apa: 'Pentina, A., & Lampert, C. (2017). Multi-task learning with labeled and
    unlabeled tasks (Vol. 70, pp. 2807–2816). Presented at the ICML: International
    Conference on Machine Learning, Sydney, Australia: ML Research Press.'
  chicago: Pentina, Anastasia, and Christoph Lampert. “Multi-Task Learning with Labeled
    and Unlabeled Tasks,” 70:2807–16. ML Research Press, 2017.
  ieee: 'A. Pentina and C. Lampert, “Multi-task learning with labeled and unlabeled
    tasks,” presented at the ICML: International Conference on Machine Learning, Sydney,
    Australia, 2017, vol. 70, pp. 2807–2816.'
  ista: 'Pentina A, Lampert C. 2017. Multi-task learning with labeled and unlabeled
    tasks. ICML: International Conference on Machine Learning, PMLR, vol. 70, 2807–2816.'
  mla: Pentina, Anastasia, and Christoph Lampert. <i>Multi-Task Learning with Labeled
    and Unlabeled Tasks</i>. Vol. 70, ML Research Press, 2017, pp. 2807–16.
  short: A. Pentina, C. Lampert, in:, ML Research Press, 2017, pp. 2807–2816.
conference:
  end_date: 2017-08-11
  location: Sydney, Australia
  name: 'ICML: International Conference on Machine Learning'
  start_date: 2017-08-06
date_created: 2018-12-11T11:49:37Z
date_published: 2017-06-08T00:00:00Z
date_updated: 2023-10-17T11:53:32Z
day: '08'
department:
- _id: ChLa
ec_funded: 1
external_id:
  isi:
  - '000683309502093'
intvolume: '        70'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/1602.06518
month: '06'
oa: 1
oa_version: Submitted Version
page: 2807 - 2816
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
  call_identifier: FP7
  grant_number: '308036'
  name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
  isbn:
  - '9781510855144'
publication_status: published
publisher: ML Research Press
publist_id: '6399'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Multi-task learning with labeled and unlabeled tasks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 70
year: '2017'
...