{"id":"W2941668132","doi":"","title":"Old Techniques in Differentially Private Linear Regression.","year":2019,"lang":"en","type":"article","venue":"Algorithmic Learning Theory","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Linear regression; Computer science; Statistics; Mathematics","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":[0.01321216,0.001263638,0.001623087,0.002285483,0.001236294,0.001914453,0.00267704,0.001877744,0.005505837],"category_scores_gemma":[0.04780585,0.0008362306,0.0009261012,0.004449903,0.003886749,0.006543621,0.003450823,0.005943289,0.0018402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002350259,"about_ca_system_score_gemma":0.001947763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001583463,"about_ca_topic_score_gemma":0.001767665,"domain_scores_codex":[0.9912496,0.005785652,0.0002350925,0.001030397,0.00140962,0.0002896533],"domain_scores_gemma":[0.9729337,0.01915073,0.000953911,0.005106527,0.001444053,0.000410965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008998645,0.00006037672,0.00115613,0.0002485767,0.0001001569,0.00003055469,0.0001285978,0.008564204,0.0002656165,0.9078033,0.009694945,0.07185762],"study_design_scores_gemma":[0.00004076565,0.00004099892,0.0004938137,0.0001000079,0.00003798132,0.00005980436,0.00003266407,0.05236636,0.0004142883,0.9327695,0.0136292,0.00001461006],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004610294,0.01025515,0.9711282,0.004896008,0.0006118642,0.00005137357,0.0003173035,0.0002040574,0.007925733],"genre_scores_gemma":[0.4076002,0.02187574,0.5214196,0.004410915,0.006920069,0.0008289725,0.001191117,0.0003818778,0.03537153],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01321216,"threshold_uncertainty_score":0.06987339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02917136203369002,"score_gpt":0.3203901574777243,"score_spread":0.2912187954440343,"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."}}