{"id":"W4396220729","doi":"10.1007/s10107-024-02078-z","title":"Sample complexity analysis for adaptive optimization algorithms with stochastic oracles","year":2024,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Stochastic Gradient Optimization Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Algorithm; Sample complexity; Stochastic optimization; Numerical analysis; Mathematical optimization; Sample (material); Computer science; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01426878,0.002673496,0.003236605,0.002831891,0.001018499,0.00414432,0.004150441,0.003362385,0.005964658],"category_scores_gemma":[0.0935134,0.001649105,0.002185001,0.002650167,0.00436075,0.009517741,0.0050436,0.008550966,0.0006785082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003813262,"about_ca_system_score_gemma":0.003363564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002983883,"about_ca_topic_score_gemma":0.002647557,"domain_scores_codex":[0.9915172,0.004098729,0.000383095,0.0008924134,0.002583162,0.0005254172],"domain_scores_gemma":[0.8751484,0.1126869,0.003118364,0.003819794,0.003830926,0.001395563],"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":[0.0003999016,0.0002369452,0.002077112,0.0004658273,0.0002111231,0.0001375735,0.0001749357,0.5233129,0.001498166,0.4337972,0.003664391,0.034024],"study_design_scores_gemma":[0.00001771197,0.00003387019,0.0002111242,0.00002042132,0.00001543527,0.00001913421,0.00001014473,0.8861894,0.0002496947,0.1129735,0.0002467658,0.00001270936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01006359,0.0008593524,0.9852446,0.00112386,0.00008668993,0.00006672628,0.0001083432,0.0001906214,0.002256233],"genre_scores_gemma":[0.6064378,0.003034299,0.37333,0.001197118,0.001277146,0.001443323,0.001146423,0.0009277832,0.01120612],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01426878,"threshold_uncertainty_score":0.07546139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05945494826338559,"score_gpt":0.2993927871325913,"score_spread":0.2399378388692057,"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."}}