{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00374903,0.001391455,0.002188876,0.0009810597,0.0006211332,0.002040317,0.002341062,0.002619182,0.004303626],"category_scores_gemma":[0.0140412,0.001282547,0.00124167,0.001226368,0.001983509,0.002374751,0.002724409,0.003492998,0.001294077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001931257,"about_ca_system_score_gemma":0.002892293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006374422,"about_ca_topic_score_gemma":0.005684368,"domain_scores_codex":[0.9978024,0.0009877875,0.00009336397,0.0003934727,0.000546549,0.0001763168],"domain_scores_gemma":[0.9943064,0.004033686,0.0003185581,0.0005163317,0.0006493173,0.0001756775],"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":[0.00009496694,0.00004127661,0.0004322384,0.0001358597,0.000058533,0.00003563177,0.00005167924,0.9101704,0.0007899506,0.04725925,0.003143756,0.03778642],"study_design_scores_gemma":[0.000009326681,0.00001192311,0.00004207753,0.000009246468,0.000005787506,0.000008387839,0.000003762972,0.9806057,0.0002674255,0.01844478,0.0005868027,0.000004744302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00388124,0.0003236019,0.9926429,0.0003020579,0.00002618993,0.00003500858,0.00008014928,0.0003557938,0.002353059],"genre_scores_gemma":[0.421702,0.0009598113,0.5668883,0.0006064594,0.0001812641,0.0003680996,0.0008936392,0.0006947517,0.007705687],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006374422,"threshold_uncertainty_score":0.01982701,"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."}}