{"id":"W3034535486","doi":"10.48550/arxiv.2006.06618","title":"CoinPress: Practical Private Mean and Covariance Estimation","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Estimator; Covariance; Multivariate statistics; Sample mean and sample covariance; A priori and a posteriori; Gaussian; Statistics; Estimation of covariance matrices; Sample (material); Sample size determination; Computer science; Covariance matrix; Multivariate normal distribution; Mathematics; Econometrics; Algorithm","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":[0.007419047,0.001199983,0.00165497,0.001699074,0.001020382,0.003856367,0.002889712,0.003259939,0.01419394],"category_scores_gemma":[0.05308019,0.0009929489,0.0008912163,0.003126223,0.002575341,0.007036547,0.006915313,0.003831608,0.00809285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001381551,"about_ca_system_score_gemma":0.002622718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008132488,"about_ca_topic_score_gemma":0.0009624913,"domain_scores_codex":[0.9920847,0.003185605,0.0004214154,0.001113855,0.002794977,0.0003995019],"domain_scores_gemma":[0.9786067,0.00764302,0.001229608,0.01035505,0.001702711,0.0004628475],"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":[0.0007553988,0.0001882566,0.002628424,0.0003797337,0.0001694902,0.0004111221,0.0002384706,0.1128897,0.007720533,0.4507479,0.06151315,0.3623579],"study_design_scores_gemma":[0.0001211088,0.00008194138,0.0004506742,0.00008573177,0.00002782032,0.0005048452,0.00004413379,0.5894378,0.01094212,0.3653747,0.03286773,0.00006134901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002639577,0.0004057373,0.98946,0.001002559,0.0001902418,0.00007386362,0.000522416,0.002410989,0.003294694],"genre_scores_gemma":[0.3232371,0.001750114,0.645164,0.002197447,0.001119273,0.001071669,0.003659748,0.001938733,0.01986199],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01419394,"threshold_uncertainty_score":0.0474835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1251456529149458,"score_gpt":0.2401299372874289,"score_spread":0.1149842843724831,"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."}}