{"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/thresholded-lasso-bandit","title":"Thresholded Lasso Bandit","arxiv_id":"2010.11994","date":"2020-10-22","proceeding":null,"authors":["Kaito Ariu","Kenshi Abe","Alexandre Proutière"],"abstract":"In this paper, we revisit the regret minimization problem in sparse stochastic contextual linear bandits, where feature vectors may be of large dimension $d$, but where the reward function depends on a few, say $s_0\\ll d$, of these features only. We present Thresholded Lasso bandit, an algorithm that (i) estimates the vector defining the reward function as well as its sparse support, i.e., significant feature elements, using the Lasso framework with thresholding, and (ii) selects an arm greedily according to this estimate projected on its support. The algorithm does not require prior knowledge of the sparsity index $s_0$ and can be parameter-free under some symmetric assumptions. For this simple algorithm, we establish non-asymptotic regret upper bounds scaling as $\\mathcal{O}( \\log d + \\sqrt{T} )$ in general, and as $\\mathcal{O}( \\log d + \\log T)$ under the so-called margin condition (a probabilistic condition on the separation of the arm rewards). The regret of previous algorithms scales as $\\mathcal{O}( \\log d + \\sqrt{T \\log (d T)})$ and $\\mathcal{O}( \\log T \\log d)$ in the two settings, respectively. Through numerical experiments, we confirm that our algorithm outperforms existing methods.","url_abs":"https://arxiv.org/abs/2010.11994v4","url_pdf":"https://arxiv.org/pdf/2010.11994v4.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":"thresholded-lasso-bandit","repo_url":"https://github.com/cyberagentailab/thresholded-lasso-bandit","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2010.11994","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.11994"}},"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/cyberagentailab/thresholded-lasso-bandit","reach":null}],"summary":{"ran_violates":1},"by_repo_kind":{"official":{"samples":1,"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":"258dc839c5504047","entry":"linear_regression","repo":"cyberagentailab/thresholded-lasso-bandit","repo_kind":"official","path":"algorithms/th_lasso_bandit.py","file_url":"https://github.com/cyberagentailab/thresholded-lasso-bandit/blob/HEAD/algorithms/th_lasso_bandit.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"258dc839c5504047"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}