{"id":"W2971343933","doi":"10.48550/arxiv.1904.07381","title":"Approximation Algorithms for Distributionally Robust Stochastic Optimization with Black-Box Distributions","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Vertex cover; Mathematical optimization; Rounding; Approximation algorithm; Robust optimization; Probability distribution; Computer science; Optimization problem; Set cover problem; Stochastic optimization; Mathematics; Algorithm; Set (abstract data type)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009701051,0.0004297055,0.0005412797,0.0004355791,0.0003967132,0.000393492,0.00101831,0.0004437873,0.0001438722],"category_scores_gemma":[0.0006583454,0.0003929391,0.000295328,0.001325679,0.0002595956,0.000638129,0.0003933273,0.0003833665,0.0001127985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003887917,"about_ca_system_score_gemma":0.0005622808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000330716,"about_ca_topic_score_gemma":0.00001964755,"domain_scores_codex":[0.9968587,0.0001750954,0.0005994524,0.001453459,0.0004892764,0.00042401],"domain_scores_gemma":[0.9951278,0.0006453813,0.00097641,0.001152835,0.001895259,0.0002023294],"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":[0.0001771978,0.0001096643,0.0006921284,0.00001727206,0.0000689493,0.000007408044,0.00005576478,0.9686415,7.569495e-7,0.02859794,0.001325452,0.0003059638],"study_design_scores_gemma":[0.0009310319,0.0001043069,0.0007031782,0.0000608171,0.0002161999,0.000003811616,0.000196729,0.9739708,0.00001023943,0.02293168,0.0003863173,0.0004848838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01039183,0.00002312273,0.9845125,0.000201173,0.0005194256,0.00155927,0.002059963,0.0001301128,0.0006025666],"genre_scores_gemma":[0.9636947,0.0001136585,0.02657406,0.00002587642,0.0001676072,0.0000144078,0.006379979,0.00003944364,0.00299029],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9579385,"threshold_uncertainty_score":0.9998522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1387307032030742,"score_gpt":0.2549357676138457,"score_spread":0.1162050644107715,"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."}}