{"id":"W3153988432","doi":"","title":"Inverse Bayesian Optimization: Learning Human Search Strategies in a Sequential Optimization Task.","year":2021,"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":"University of Toronto","funders":"","keywords":"Bayesian optimization; Hyperparameter optimization; Probabilistic logic; Computer science; Bayesian probability; Task (project management); Function (biology); Machine learning; Optimization problem; Range (aeronautics); Artificial intelligence; Mathematical optimization; Algorithm; Mathematics; Engineering","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":[0.0003483393,0.0004785266,0.0004780105,0.0008008359,0.0003893125,0.0006908613,0.001356013,0.0004414577,0.0001983321],"category_scores_gemma":[0.00007060589,0.0006595769,0.0001820044,0.001985872,0.0001828027,0.002140853,0.002157278,0.001275928,0.0000107432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007668812,"about_ca_system_score_gemma":0.0008154694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002870717,"about_ca_topic_score_gemma":0.0002088585,"domain_scores_codex":[0.996398,0.0006648902,0.0004132313,0.001747607,0.000231592,0.0005447096],"domain_scores_gemma":[0.9977773,0.00007777172,0.0003496967,0.000973091,0.0006122838,0.0002098797],"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":[0.00001252843,0.0001163944,0.0004948971,0.00005357777,0.00004900878,0.0006079925,0.001143268,0.9868201,0.00002916291,0.01056313,0.000005944263,0.0001039838],"study_design_scores_gemma":[0.0009814976,0.00004813615,0.00007313794,0.0001297019,0.00002629852,0.00000935277,0.001723708,0.995521,0.00006381018,0.0007986934,0.00001087084,0.0006137621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005465846,0.0000298908,0.9911238,0.00005577129,0.0003731817,0.0004913957,0.000005773567,0.0003966794,0.002057694],"genre_scores_gemma":[0.6581299,0.00020065,0.3405195,0.00003837511,0.00006702619,0.000004112025,0.0002287129,0.00004534709,0.0007662596],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6526641,"threshold_uncertainty_score":0.9995856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05935918226286159,"score_gpt":0.2331094377846446,"score_spread":0.173750255521783,"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."}}