{"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/leveraging-instance-features-for-label","title":"Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision","arxiv_id":"2210.02724","date":"2022-10-06","proceeding":null,"authors":["Jieyu Zhang","Linxin Song","Alexander Ratner"],"abstract":"Programmatic Weak Supervision (PWS) has emerged as a widespread paradigm to synthesize training labels efficiently. The core component of PWS is the label model, which infers true labels by aggregating the outputs of multiple noisy supervision sources abstracted as labeling functions (LFs). Existing statistical label models typically rely only on the outputs of LF, ignoring the instance features when modeling the underlying generative process. In this paper, we attempt to incorporate the instance features into a statistical label model via the proposed FABLE. In particular, it is built on a mixture of Bayesian label models, each corresponding to a global pattern of correlation, and the coefficients of the mixture components are predicted by a Gaussian Process classifier based on instance features. We adopt an auxiliary variable-based variational inference algorithm to tackle the non-conjugate issue between the Gaussian Process and Bayesian label models. Extensive empirical comparison on eleven benchmark datasets sees FABLE achieving the highest averaged performance across nine baselines.","url_abs":"https://arxiv.org/abs/2210.02724v2","url_pdf":"https://arxiv.org/pdf/2210.02724v2.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":"leveraging-instance-features-for-label","repo_url":"https://github.com/JieyuZ2/wrench/blob/main/wrench/labelmodel/fable.py","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"leveraging-instance-features-for-label","repo_url":"https://github.com/jieyuz2/wrench","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"},{"method_slug":"variational-inference","method_name":"Variational Inference"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2210.02724","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02724"}},"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/jieyuz2/wrench","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/JieyuZ2/wrench/blob/main/wrench/labelmodel/fable.py","reach":null}],"summary":{"ran_honours":1,"unverified":6},"by_repo_kind":{"official":{"samples":7,"ran":1,"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":"ffcb36e0c6ef81dd","entry":"random_embedding","repo":"JieyuZ2/wrench","repo_kind":"official","path":"wrench/layers.py","file_url":"https://github.com/JieyuZ2/wrench/blob/HEAD/wrench/layers.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"ffcb36e0c6ef81dd"}},{"code_sha256_prefix":"ad14014186a0bf9e","entry":"calc_loss","repo":"JieyuZ2/wrench","repo_kind":"official","path":"wrench/endmodel/cosine.py","file_url":"https://github.com/JieyuZ2/wrench/blob/HEAD/wrench/endmodel/cosine.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"ad14014186a0bf9e"}},{"code_sha256_prefix":"3fa1363dd8984924","entry":"calc_prior","repo":"JieyuZ2/wrench","repo_kind":"official","path":"wrench/endmodel/ars2.py","file_url":"https://github.com/JieyuZ2/wrench/blob/HEAD/wrench/endmodel/ars2.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"3fa1363dd8984924"}},{"code_sha256_prefix":"c9a213796a572f53","entry":"check_bert_model","repo":"JieyuZ2/wrench","repo_kind":"official","path":"wrench/basemodel.py","file_url":"https://github.com/JieyuZ2/wrench/blob/HEAD/wrench/basemodel.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"c9a213796a572f53"}},{"code_sha256_prefix":"4ac4f520b6d01aa2","entry":"check_vision_model","repo":"JieyuZ2/wrench","repo_kind":"official","path":"wrench/basemodel.py","file_url":"https://github.com/JieyuZ2/wrench/blob/HEAD/wrench/basemodel.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"4ac4f520b6d01aa2"}},{"code_sha256_prefix":"8c515f7346076b72","entry":"contrastive_loss","repo":"JieyuZ2/wrench","repo_kind":"official","path":"wrench/endmodel/cosine.py","file_url":"https://github.com/JieyuZ2/wrench/blob/HEAD/wrench/endmodel/cosine.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"8c515f7346076b72"}},{"code_sha256_prefix":"8b6fde8bcb2e818b","entry":"soft_frequency","repo":"JieyuZ2/wrench","repo_kind":"official","path":"wrench/endmodel/cosine.py","file_url":"https://github.com/JieyuZ2/wrench/blob/HEAD/wrench/endmodel/cosine.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"8b6fde8bcb2e818b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}