{"id":"W4381568467","doi":"10.48550/arxiv.2306.10835","title":"Online Dynamic Submodular Optimization","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données","keywords":"Submodular set function; Regret; Mathematical optimization; Online algorithm; Computer science; Gradient descent; Function (biology); Optimization problem; Time horizon; Upper and lower bounds; Approximation algorithm; Mathematics; Artificial intelligence; Artificial neural network","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001326512,0.0003581491,0.0005350491,0.001214918,0.0002349764,0.0002357819,0.002686712,0.0004321431,0.0005034986],"category_scores_gemma":[0.001906555,0.0003619537,0.0003190786,0.002517841,0.0002719149,0.0004223009,0.002994379,0.0009883527,0.001240926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004664968,"about_ca_system_score_gemma":0.0003052284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001017927,"about_ca_topic_score_gemma":0.0002316252,"domain_scores_codex":[0.9959151,0.0003652439,0.0004958763,0.001923348,0.0007428232,0.0005575969],"domain_scores_gemma":[0.9955714,0.0008389128,0.0004149118,0.002016937,0.0008905636,0.0002673252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003449785,0.00009105774,0.0005746023,0.00001267967,0.00004111973,0.000407684,0.00003962437,0.9969435,0.000005969545,0.0003416149,0.0005001582,0.001007465],"study_design_scores_gemma":[0.0003513,0.00003249852,0.002137176,0.00003634104,0.00002692083,0.000002203623,0.0002991288,0.928592,0.0000125103,0.06767393,0.0004927688,0.0003432831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07894114,0.00005162872,0.9186133,0.0002505626,0.0009333426,0.0004196654,0.0002873226,0.0003129434,0.0001900993],"genre_scores_gemma":[0.9557642,0.0005877046,0.005281045,0.00003586931,0.0001369788,0.000001576551,0.0003446603,0.00007279304,0.03777518],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9133323,"threshold_uncertainty_score":0.9998832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3093324176986729,"score_gpt":0.330028422291579,"score_spread":0.0206960045929061,"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."}}