{"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/constrained-labeling-for-weakly-supervised","title":"Constrained Labeling for Weakly Supervised Learning","arxiv_id":"2009.07360","date":"2020-09-15","proceeding":null,"authors":["Chidubem Arachie","Bert Huang"],"abstract":"Curation of large fully supervised datasets has become one of the major roadblocks for machine learning. Weak supervision provides an alternative to supervised learning by training with cheap, noisy, and possibly correlated labeling functions from varying sources. The key challenge in weakly supervised learning is combining the different weak supervision signals while navigating misleading correlations in their errors. In this paper, we propose a simple data-free approach for combining weak supervision signals by defining a constrained space for the possible labels of the weak signals and training with a random labeling within this constrained space. Our method is efficient and stable, converging after a few iterations of gradient descent. We prove theoretical conditions under which the worst-case error of the randomized label decreases with the rank of the linear constraints. We show experimentally that our method outperforms other weak supervision methods on various text- and image-classification tasks.","url_abs":"https://arxiv.org/abs/2009.07360v5","url_pdf":"https://arxiv.org/pdf/2009.07360v5.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":"constrained-labeling-for-weakly-supervised","repo_url":"https://github.com/VTCSML/Constrained-Labeling-for-Weakly-Supervised-Learning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"weakly-supervised-learning","task_name":"Weakly-supervised Learning"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2009.07360","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.07360"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/VTCSML/Constrained-Labeling-for-Weakly-Supervised-Learning","reach":null}],"summary":{"ran_honours":1,"ran_fixture":2},"by_repo_kind":{"official":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"736fdbe09b7366e8","entry":"bound_loss","repo":"VTCSML/Constrained-Labeling-for-Weakly-Supervised-Learning","repo_kind":"official","path":"train_CLL.py","file_url":"https://github.com/VTCSML/Constrained-Labeling-for-Weakly-Supervised-Learning/blob/HEAD/train_CLL.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"736fdbe09b7366e8"}},{"code_sha256_prefix":"4288c0336b99d805","entry":"run_constraints","repo":"VTCSML/Constrained-Labeling-for-Weakly-Supervised-Learning","repo_kind":"official","path":"train_CLL.py","file_url":"https://github.com/VTCSML/Constrained-Labeling-for-Weakly-Supervised-Learning/blob/HEAD/train_CLL.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4288c0336b99d805"}},{"code_sha256_prefix":"b3d20b0b154c5050","entry":"y_gradient","repo":"VTCSML/Constrained-Labeling-for-Weakly-Supervised-Learning","repo_kind":"official","path":"train_CLL.py","file_url":"https://github.com/VTCSML/Constrained-Labeling-for-Weakly-Supervised-Learning/blob/HEAD/train_CLL.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b3d20b0b154c5050"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}