{"id":"W2891786606","doi":"","title":"Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes","year":2018,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Waterloo; McMaster University","funders":"","keywords":"Upper and lower bounds; Compression (physics); Sample complexity; Mixture model; Matching (statistics); Sample (material); Mathematics; Data compression; Combinatorics; Algorithm; Computer science; Statistics; Artificial intelligence; Physics; Mathematical analysis","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":[0.009253108,0.002614577,0.003129089,0.002399551,0.001341069,0.003959379,0.003853357,0.003249112,0.006587107],"category_scores_gemma":[0.08013211,0.001431673,0.002249527,0.002555255,0.004224103,0.01344106,0.007737068,0.008107519,0.001603393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004113507,"about_ca_system_score_gemma":0.002918225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002033097,"about_ca_topic_score_gemma":0.002266272,"domain_scores_codex":[0.9926009,0.002092334,0.0005078184,0.001273985,0.002722035,0.0008029709],"domain_scores_gemma":[0.9294038,0.05844741,0.002400253,0.006008429,0.002388887,0.001351199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001082514,0.0004002028,0.004108725,0.0005532887,0.0002158018,0.000255773,0.000425713,0.5813641,0.007383003,0.282316,0.006398329,0.1154966],"study_design_scores_gemma":[0.00003978454,0.00006825681,0.0003385808,0.00004129106,0.00002062627,0.00008384089,0.00002575146,0.8987802,0.001932339,0.09769797,0.0009459882,0.00002537099],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0291366,0.001500543,0.9612257,0.001653129,0.00008392094,0.0001648468,0.0003420679,0.0008718448,0.005021436],"genre_scores_gemma":[0.4992166,0.00242896,0.4863064,0.001529814,0.000710804,0.001183673,0.001896197,0.0007827876,0.005944677],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009253108,"threshold_uncertainty_score":0.04893571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0251193021852036,"score_gpt":0.2861917601460713,"score_spread":0.2610724579608677,"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."}}