{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/patterns-for-learning-with-side-information","title":"Patterns for Learning with Side Information","arxiv_id":"1511.06429","date":"2015-11-19","proceeding":null,"authors":["Rico Jonschkowski","Sebastian Höfer","Oliver Brock"],"abstract":"Supervised, semi-supervised, and unsupervised learning estimate a function\ngiven input/output samples. Generalization of the learned function to unseen\ndata can be improved by incorporating side information into learning. Side\ninformation are data that are neither from the input space nor from the output\nspace of the function, but include useful information for learning it. In this\npaper we show that learning with side information subsumes a variety of related\napproaches, e.g. multi-task learning, multi-view learning and learning using\nprivileged information. Our main contributions are (i) a new perspective that\nconnects these previously isolated approaches, (ii) insights about how these\nmethods incorporate different types of prior knowledge, and hence implement\ndifferent patterns, (iii) facilitating the application of these methods in\nnovel tasks, as well as (iv) a systematic experimental evaluation of these\npatterns in two supervised learning tasks.","url_abs":"http://arxiv.org/abs/1511.06429v5","url_pdf":"http://arxiv.org/pdf/1511.06429v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"patterns-for-learning-with-side-information","repo_url":"https://github.com/tu-rbo/concarne","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"multi-view-learning","task_name":"MULTI-VIEW LEARNING"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1511.06429","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1511.06429"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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