{"id":"W2770008080","doi":"","title":"Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation","year":2017,"lang":"en","type":"article","venue":"neural information processing systems","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Regret; Sublinear function; Computer science; Algorithm; Linear regression; Constraint (computer-aided design); Task (project management); Exponential function; Mathematical optimization; Mathematics; Machine learning; Discrete mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002274249,0.0003218286,0.0004885221,0.0005060181,0.001776069,0.003127303,0.001309996,0.0001847632,0.00001120056],"category_scores_gemma":[0.004896169,0.0001957884,0.00009859134,0.0006779463,0.0002326,0.004613444,0.0001784435,0.0003298494,0.0000541893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001248813,"about_ca_system_score_gemma":0.0002437004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005906789,"about_ca_topic_score_gemma":0.00001503334,"domain_scores_codex":[0.9949041,0.0001075914,0.001371098,0.000465812,0.002604946,0.0005464558],"domain_scores_gemma":[0.9924403,0.0004285552,0.002135972,0.001119859,0.003659756,0.0002155705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007516705,0.0001569789,0.002441657,0.0003710793,0.00002244177,0.000008401428,0.001241548,0.4708841,0.0001824421,0.00009444678,0.002654012,0.5211912],"study_design_scores_gemma":[0.001505864,0.0002177994,0.003530876,0.0002833797,0.000009897922,0.00003426371,0.0008152613,0.9730873,0.0003388921,0.00006304462,0.01984886,0.0002646107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3114899,0.0001963521,0.6813874,0.002549924,0.001465746,0.002172421,0.0002690699,0.0002801139,0.000189163],"genre_scores_gemma":[0.968922,0.000007079455,0.02724859,0.0001950921,0.0007165697,0.0002013537,0.0003934506,0.00004125137,0.002274651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6574321,"threshold_uncertainty_score":0.9995235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2585063217238566,"score_gpt":0.4470429972754272,"score_spread":0.1885366755515706,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}