{"id":"W4399455309","doi":"10.48550/arxiv.2406.04308","title":"Approximation-Aware Bayesian Optimization","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Biological Infrastructure; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; University of Pennsylvania; National Science Foundation","keywords":"Bayesian optimization; Bayesian probability; Computer science; Econometrics; Artificial intelligence; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001872595,0.0002530373,0.0002095742,0.0002915995,0.0001384301,0.0003074291,0.001209504,0.0002213746,0.00006066151],"category_scores_gemma":[0.00002633635,0.0002816662,0.000158004,0.0006350991,0.0000392406,0.0001982128,0.001981495,0.000778198,0.0001697354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001179473,"about_ca_system_score_gemma":0.0001604211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000823734,"about_ca_topic_score_gemma":0.000004014511,"domain_scores_codex":[0.9984187,0.0001128908,0.0001542631,0.0009822156,0.00009434282,0.0002375311],"domain_scores_gemma":[0.9987062,0.00004480995,0.0001370265,0.0008594369,0.0001338321,0.0001186867],"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":[0.000001727181,0.00002232432,0.00009522865,0.0001020281,0.00003312685,0.0001112105,0.0001559103,0.9199285,7.012029e-7,0.0775372,0.0003706803,0.001641369],"study_design_scores_gemma":[0.0001128107,0.00001727048,0.0000270731,0.00009910445,0.00003271167,0.000004523418,0.00002414212,0.9661351,0.00001174897,0.03298796,0.0002520424,0.0002955328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001470153,0.00005772435,0.9910832,0.0004259262,0.000941555,0.0001743178,0.000008908407,0.000756508,0.005081708],"genre_scores_gemma":[0.960213,0.00004758078,0.0345898,0.0001006151,0.0001683656,0.000001239702,0.00005234753,0.00002470521,0.004802319],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9587429,"threshold_uncertainty_score":0.9999635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03172677665055935,"score_gpt":0.1834865916528229,"score_spread":0.1517598150022636,"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."}}