{"id":"W2622730215","doi":"","title":"Discovering and Exploiting Additive Structure for Bayesian Optimization","year":2017,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Computer science; Bayesian probability; Bayesian optimization; Artificial intelligence; Data mining; 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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0001099467,0.0001489277,0.0001336606,0.00007199556,0.0005189747,0.001644422,0.0005623475,0.00005373169,0.00006571144],"category_scores_gemma":[0.0005384061,0.0001393455,0.00001740341,0.00002923596,0.0001649181,0.0006806724,0.0001680048,0.0001053114,0.000002617309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002002152,"about_ca_system_score_gemma":0.00006416276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002980078,"about_ca_topic_score_gemma":0.00007591552,"domain_scores_codex":[0.9989836,0.00001644947,0.0002459218,0.0003824754,0.0001900654,0.0001814794],"domain_scores_gemma":[0.9990045,0.0001868427,0.0002178368,0.000228529,0.0002784054,0.00008382262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002033594,0.0000112607,0.0000451669,0.000009853128,0.000009537784,0.00000427222,0.0003255765,0.0003810491,0.0001119154,0.7500687,0.00001965084,0.2489927],"study_design_scores_gemma":[0.00003227192,0.00008702507,0.0002150643,0.00005854385,0.000004043278,0.000006972921,0.0002359012,0.6418862,0.001497781,0.3557843,0.00005530527,0.0001365693],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000682515,0.000007214557,0.9950086,0.00166119,0.000380085,0.0001402207,0.0003908032,0.00002510136,0.001704245],"genre_scores_gemma":[0.7770833,0.0001152906,0.2225188,0.00009786808,0.00008976611,0.00001337603,0.00002573492,0.000006235746,0.00004969298],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7764007,"threshold_uncertainty_score":0.999392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.074198638650729,"score_gpt":0.3368367995968381,"score_spread":0.2626381609461091,"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."}}