{"id":"W2920848790","doi":"10.1007/978-3-030-29135-8_8","title":"AutoML @ NeurIPS 2018 Challenge: Design and Results","year":2019,"lang":"en","type":"book-chapter","venue":"˜The œSpringer series on challenges in machine learning","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Acadia University","funders":"Microsoft Research","keywords":"Lifelong learning; Competition (biology); Computer science; Duration (music); Artificial intelligence; Autonomous learning; Data science; Machine learning; Mathematics education; Psychology; Pedagogy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007146926,0.00170093,0.001316787,0.001121236,0.001978362,0.005723679,0.003400396,0.003071973,0.06353492],"category_scores_gemma":[0.01859645,0.0004568042,0.0009048721,0.001084501,0.001448669,0.006572893,0.006110867,0.00368358,0.07729631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002652,"about_ca_system_score_gemma":0.004779455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003981466,"about_ca_topic_score_gemma":0.005859489,"domain_scores_codex":[0.9940475,0.0018835,0.0001940625,0.0008532991,0.002178315,0.0008433278],"domain_scores_gemma":[0.9925333,0.001586485,0.0001782351,0.00114426,0.003027342,0.00153035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001882032,0.000756873,0.0008174228,0.0008659656,0.0000512228,0.00008134362,0.0001137666,0.003734803,0.002895097,0.01522445,0.8344492,0.1391279],"study_design_scores_gemma":[0.001455929,0.002176305,0.00276734,0.0004038794,0.0001199613,0.0004288847,0.0007794026,0.07649241,0.0212711,0.04141854,0.8525335,0.0001526862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.09059637,0.01776994,0.180489,0.04900708,0.02523638,0.007620962,0.0723443,0.04491701,0.512019],"genre_scores_gemma":[0.2538121,0.005120127,0.1851218,0.01492102,0.004311193,0.01384388,0.1497955,0.0142368,0.3588374],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06353492,"threshold_uncertainty_score":0.2125455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05760360569810447,"score_gpt":0.2601444043592527,"score_spread":0.2025407986611483,"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."}}