{"id":"W2949103490","doi":"10.48550/arxiv.1003.0804","title":"Branch and Bound Algorithms for Maximizing Expected Improvement Functions","year":2010,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Algorithm; Function (biology); Set (abstract data type); Mathematical optimization; Source code; Process (computing); Genetic algorithm; Code (set theory); Gaussian process; Gaussian; Computer experiment; Simulation; Mathematics; Machine learning","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":[0.0001709728,0.0003974012,0.0003548354,0.0003338981,0.0004648262,0.0002541123,0.0008849904,0.0003494211,0.00001868726],"category_scores_gemma":[0.0000743303,0.0004809679,0.0001669059,0.000473654,0.0001596321,0.0006174631,0.001502741,0.0006530981,0.00001159415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002370624,"about_ca_system_score_gemma":0.0001919587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006536144,"about_ca_topic_score_gemma":0.00005208396,"domain_scores_codex":[0.9976504,0.00004697983,0.0002474553,0.001518426,0.00009974313,0.0004370069],"domain_scores_gemma":[0.9977988,0.0001537307,0.0003154237,0.001041827,0.0004695787,0.0002206138],"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":[0.0001689555,0.0007546777,0.0007939776,0.0003236718,0.0007098414,0.000156763,0.001706455,0.7683599,0.004882056,0.1038605,0.0004522035,0.117831],"study_design_scores_gemma":[0.001450155,0.0001080555,0.0003362457,0.00003848968,0.00006494651,0.00000725406,0.000116846,0.9776915,0.0007695334,0.017773,0.001016377,0.000627542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02024299,0.00005861696,0.9762778,0.0001217053,0.00153908,0.0009758103,0.0000626725,0.0003832379,0.0003381383],"genre_scores_gemma":[0.7013569,0.0001359679,0.2952723,0.0001351922,0.0002073689,0.00002881838,0.00007165359,0.00005489142,0.002736949],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6811139,"threshold_uncertainty_score":0.9997642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05901204504488729,"score_gpt":0.2141443307221525,"score_spread":0.1551322856772652,"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."}}