{"id":"W2621731111","doi":"10.1609/aaai.v32i1.11627","title":"Sample-Efficient Learning of Mixtures","year":2018,"lang":"en","type":"preprint","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sample complexity; Combinatorics; Distribution (mathematics); Logarithm; Class (philosophy); Mathematics; Upper and lower bounds; Discrete mathematics; Mathematical analysis; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001056598,0.0004191868,0.0005840354,0.0002567464,0.0002370139,0.0003006776,0.004286104,0.0002639951,0.00009818826],"category_scores_gemma":[0.001849269,0.0003126001,0.000313838,0.0005647873,0.0005264977,0.00009128518,0.002731698,0.001388898,0.00004605392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004526049,"about_ca_system_score_gemma":0.0002256694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002401217,"about_ca_topic_score_gemma":0.000001228782,"domain_scores_codex":[0.9968235,0.00006184967,0.0008971671,0.000884976,0.000897789,0.0004347542],"domain_scores_gemma":[0.9963377,0.0002629704,0.00138177,0.0006024282,0.001308943,0.0001061676],"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":[0.00004469099,0.0002296882,0.0003355911,0.0002895691,0.00004826088,4.177088e-7,0.003099609,0.007182986,0.007811511,0.9012913,0.0001592135,0.07950721],"study_design_scores_gemma":[0.00001583396,0.0002192822,0.0001242967,0.0007420127,0.00001856812,0.000002056483,0.0001403884,0.642589,0.2447764,0.110974,0.0001238193,0.0002743806],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1839093,0.0001077009,0.7964377,0.003046196,0.002482528,0.0007832245,0.00002519736,0.0002925763,0.0129155],"genre_scores_gemma":[0.9828087,0.00003861426,0.01658598,0.00006311487,0.0002221819,0.0000225208,0.000001776208,0.00002204047,0.0002350607],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7988994,"threshold_uncertainty_score":0.9999326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05839631489145449,"score_gpt":0.3098292682075604,"score_spread":0.2514329533161059,"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."}}