--- _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. Multi-Task Learning with Labeled and Unlabeled Tasks. 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' ...