{"id":"W2941381621","doi":"10.1145/3313276.3316391","title":"Approximation algorithms for distributionally-robust stochastic optimization with black-box distributions","year":2019,"lang":"en","type":"article","venue":"","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Probability distribution; Mathematical optimization; Stochastic optimization; Computer science; Robust optimization; Stochastic programming; Black box; Optimization problem; Algorithm; Mathematics; Artificial intelligence; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005430455,0.001827172,0.002316747,0.001319953,0.0005391889,0.002130073,0.002262113,0.002243702,0.005168236],"category_scores_gemma":[0.01648788,0.001019048,0.001570645,0.0017842,0.001499252,0.00285963,0.002699765,0.004027152,0.001089374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002404413,"about_ca_system_score_gemma":0.00232002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005779268,"about_ca_topic_score_gemma":0.005226069,"domain_scores_codex":[0.9982368,0.0008708747,0.00008883522,0.0002527696,0.0003686323,0.000181956],"domain_scores_gemma":[0.9913372,0.007134364,0.000418665,0.000405222,0.0005166538,0.0001878187],"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":[0.00004970624,0.0000431305,0.0003510441,0.00009346212,0.00005536037,0.00003579267,0.00005348066,0.8801191,0.0002508187,0.09454188,0.001635018,0.02277117],"study_design_scores_gemma":[0.000006143192,0.000007654621,0.00002445105,0.00001181575,0.000004186122,0.000006712755,0.00000439976,0.9634145,0.00005815722,0.0360642,0.0003941607,0.00000353794],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001742959,0.0004208139,0.9962268,0.0001743191,0.00002460274,0.00001953081,0.00004408174,0.0001259747,0.001221063],"genre_scores_gemma":[0.306422,0.001875086,0.6803085,0.0004654006,0.0002454304,0.0005184185,0.0007384463,0.0005079201,0.008918876],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005779268,"threshold_uncertainty_score":0.02871937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05390955991847635,"score_gpt":0.3232666681542887,"score_spread":0.2693571082358124,"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."}}