{"id":"W2339371659","doi":"10.1007/s13171-018-0143-9","title":"Inference on Covariance Operators via Concentration Inequalities: k-sample Tests, Classification, and Clustering via Rademacher Complexities","year":2018,"lang":"en","type":"article","venue":"Sankhya A","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Engineering and Physical Sciences Research Council","keywords":"Covariance; Inference; Cluster analysis; Mathematics; Classifier (UML); Maximization; Pattern recognition (psychology); Rational quadratic covariance function; Sample (material); Analysis of covariance; Covariance function; Artificial intelligence; Statistics; Computer science; Covariance intersection; Mathematical optimization","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":[],"consensus_categories":[],"category_scores_codex":[0.000426857,0.0001860393,0.0002051753,0.00005223305,0.0002566893,0.0002388755,0.0003845712,0.00009108232,0.00004722437],"category_scores_gemma":[0.0001524192,0.0001676173,0.00002687076,0.0002380199,0.0002251272,0.0004668592,0.0001234812,0.0001378513,0.00002276103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004811752,"about_ca_system_score_gemma":0.00007220041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001363039,"about_ca_topic_score_gemma":0.00004691925,"domain_scores_codex":[0.9985484,0.0002173343,0.0002981825,0.000451182,0.0002027748,0.0002820778],"domain_scores_gemma":[0.9988621,0.0002926553,0.0001118229,0.0004760733,0.0001486208,0.0001086733],"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":[0.00002003591,0.00005890966,0.001966964,0.00005474579,0.00002651247,0.000002535871,0.007384927,0.00002631349,0.008213519,0.8804429,0.000497012,0.1013057],"study_design_scores_gemma":[0.0006206894,0.000416993,0.01251333,0.0001317353,0.00001257487,0.00001949759,0.0001230917,0.7622235,0.004195667,0.2137181,0.005430948,0.0005938091],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005873894,0.0001076872,0.9907787,0.000815833,0.0003143883,0.0002089935,0.000009648311,0.0001302216,0.001760575],"genre_scores_gemma":[0.6705554,0.00001354818,0.3280063,0.001157601,0.0001683519,0.0000204882,0.000005451088,0.000009030256,0.0000638052],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7621973,"threshold_uncertainty_score":0.6835237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06950374276023197,"score_gpt":0.3278919004414756,"score_spread":0.2583881576812437,"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."}}