{"id":"W2266817871","doi":"","title":"An expectation-maximization algorithm to compute a stochastic factorization from data","year":2015,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Stochastic matrix; Markov chain; Factorization; Algorithm; Matrix (chemical analysis); Computer science; Matrix decomposition; Matrix multiplication; Probabilistic logic; Mathematical optimization; Markov decision process; Markov process; Mathematics; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002934909,0.001831774,0.001533216,0.001229008,0.0006820237,0.001182392,0.001940244,0.001428597,0.006992678],"category_scores_gemma":[0.009147091,0.001088454,0.001379553,0.001579504,0.001042781,0.00172893,0.001824423,0.003282177,0.002385388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001226592,"about_ca_system_score_gemma":0.003184216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006458485,"about_ca_topic_score_gemma":0.008442705,"domain_scores_codex":[0.9988773,0.000456531,0.00008552792,0.0002660677,0.0002341581,0.00008047579],"domain_scores_gemma":[0.996799,0.002450673,0.0001658544,0.0002103859,0.0002920212,0.00008199381],"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":[0.0001919449,0.0002525224,0.001330362,0.0003402938,0.0002807191,0.0001611343,0.0001589209,0.5769126,0.004933702,0.04957493,0.009350187,0.3565127],"study_design_scores_gemma":[0.0000245254,0.00002761217,0.00008626068,0.00001174656,0.00001341317,0.000032002,0.000009426876,0.9802202,0.0007372819,0.01761817,0.001209409,0.000009943177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007739571,0.00003854053,0.9984156,0.00007393684,0.0000163764,0.00003652272,0.00003506934,0.0003914983,0.0002184409],"genre_scores_gemma":[0.04158413,0.0001286171,0.9561656,0.0001446832,0.00005665957,0.000406089,0.0003756707,0.0001416548,0.000996871],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006992678,"threshold_uncertainty_score":0.0233928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6099591275582319,"score_gpt":0.5389729520319333,"score_spread":0.0709861755262986,"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."}}