{"id":"W4391922577","doi":"10.48550/arxiv.2402.11981","title":"Universal generalization guarantees for Wasserstein distributionally robust models","year":2024,"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":"Discovery Air (Canada)","funders":"","keywords":"Generalization; Mathematical optimization; Computer science; Mathematics; Mathematical economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009639556,0.002529177,0.002281702,0.001441552,0.0009390523,0.00227807,0.002869102,0.002965434,0.003352851],"category_scores_gemma":[0.03845991,0.001026358,0.002263824,0.001331425,0.004061616,0.007196466,0.006870126,0.00610381,0.0005975774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003102179,"about_ca_system_score_gemma":0.001604752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002422221,"about_ca_topic_score_gemma":0.001917489,"domain_scores_codex":[0.9958402,0.001568481,0.000241222,0.0009771643,0.0009860218,0.0003869836],"domain_scores_gemma":[0.9815494,0.01088034,0.002919187,0.002588771,0.001227055,0.0008352539],"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":[0.00006627734,0.00004932839,0.001039918,0.0001665626,0.000111844,0.0001337796,0.0001606495,0.5086611,0.001732183,0.4694947,0.00240967,0.01597408],"study_design_scores_gemma":[0.000005976585,0.0000296939,0.0002282632,0.00003202483,0.00001194057,0.00004000494,0.00001481847,0.6772369,0.0003795865,0.3214722,0.0005310316,0.00001752505],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03476689,0.001033532,0.9566243,0.001688236,0.00004379903,0.00003549011,0.000264084,0.0004234649,0.005120086],"genre_scores_gemma":[0.9000653,0.00223846,0.088405,0.001050637,0.0003144694,0.0003125481,0.0007909549,0.0006490442,0.006173648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009639556,"threshold_uncertainty_score":0.0509795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2310189599451867,"score_gpt":0.2652239144553744,"score_spread":0.03420495451018762,"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."}}