{"id":"W6996226700","doi":"","title":"Sample Average Approximation for Conditional Stochastic Optimization with Dependent Data","year":2024,"lang":"en","type":"other","venue":"Open Access at Essex (University of Essex)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta; Higher Education Discipline Innovation Project; Alberta Machine Intelligence Institute; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Conditional independence; Independence (probability theory); Consistency (knowledge bases); Sample (material); Stochastic optimization; Covariance; Sample mean and sample covariance; Sampling (signal processing); Sample size determination; Optimization problem","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":[0.004443886,0.001694411,0.002005261,0.001110459,0.0005512686,0.001379435,0.001812058,0.001524626,0.003515689],"category_scores_gemma":[0.01749973,0.0008730752,0.00150427,0.001276982,0.001554374,0.00196273,0.002341324,0.004211566,0.0005913844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001837066,"about_ca_system_score_gemma":0.003126559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0080921,"about_ca_topic_score_gemma":0.007674109,"domain_scores_codex":[0.9982522,0.0007422162,0.00009190296,0.0003099954,0.0004461265,0.0001575241],"domain_scores_gemma":[0.988948,0.009019345,0.0004996587,0.0005462639,0.0007487432,0.0002380494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001318979,0.00007760652,0.001023187,0.0001759248,0.0001053366,0.00006008926,0.00005528106,0.9108252,0.000901329,0.05723509,0.001704085,0.027705],"study_design_scores_gemma":[0.000003645433,0.00000850527,0.00004057235,0.000004391926,0.000003074754,0.000003667674,0.000002168789,0.9928126,0.0001078106,0.006856971,0.0001543487,0.000002286359],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004127863,0.0002242048,0.9943442,0.000174421,0.00003037644,0.00003049861,0.00007366105,0.0001859482,0.0008089069],"genre_scores_gemma":[0.5090798,0.001118636,0.4815317,0.0004616886,0.0002396573,0.000586933,0.001292761,0.0004143549,0.005274467],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0080921,"threshold_uncertainty_score":0.02350181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09183770879828002,"score_gpt":0.3433401446018742,"score_spread":0.2515024358035942,"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."}}