{"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/contextual-semibandits-via-supervised","title":"Contextual Semibandits via Supervised Learning Oracles","arxiv_id":"1502.05890","date":"2015-02-20","proceeding":"NeurIPS 2016 12","authors":["Akshay Krishnamurthy","Alekh Agarwal","Miroslav Dudik"],"abstract":"We study an online decision making problem where on each round a learner\nchooses a list of items based on some side information, receives a scalar\nfeedback value for each individual item, and a reward that is linearly related\nto this feedback. These problems, known as contextual semibandits, arise in\ncrowdsourcing, recommendation, and many other domains. This paper reduces\ncontextual semibandits to supervised learning, allowing us to leverage powerful\nsupervised learning methods in this partial-feedback setting. Our first\nreduction applies when the mapping from feedback to reward is known and leads\nto a computationally efficient algorithm with near-optimal regret. We show that\nthis algorithm outperforms state-of-the-art approaches on real-world\nlearning-to-rank datasets, demonstrating the advantage of oracle-based\nalgorithms. Our second reduction applies to the previously unstudied setting\nwhen the linear mapping from feedback to reward is unknown. Our regret\nguarantees are superior to prior techniques that ignore the feedback.","url_abs":"http://arxiv.org/abs/1502.05890v4","url_pdf":"http://arxiv.org/pdf/1502.05890v4.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":"contextual-semibandits-via-supervised","repo_url":"https://github.com/akshaykr/oracle_cb","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"learning-to-rank","task_name":"Learning-To-Rank"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1502.05890","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1502.05890"}},"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/akshaykr/oracle_cb","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{},"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":1,"samples":[{"code_sha256_prefix":"d8005cc7d8d19e00","entry":"truncate_context","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"d8005cc7d8d19e00"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}