{"id":"W2111272908","doi":"10.48550/arxiv.1206.4650","title":"Analysis of Kernel Mean Matching under Covariate Shift","year":2012,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Covariate; Estimator; Kernel (algebra); Statistics; Mathematics; Matching (statistics); Kernel density estimation; Variable kernel density estimation; Computer science; Econometrics; Kernel method; Artificial intelligence; Support vector machine","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":[0.0002119909,0.00009098506,0.0001677843,0.0003002818,0.00008199693,0.00002391199,0.0004264444,0.00005679204,0.00009587519],"category_scores_gemma":[0.000005138523,0.00009275148,0.0001393055,0.001279615,0.00003054271,0.0008121488,0.0001862361,0.00007723684,0.00009232066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003134336,"about_ca_system_score_gemma":0.00001742094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001860268,"about_ca_topic_score_gemma":0.00003594431,"domain_scores_codex":[0.9992774,0.00007310611,0.0001110389,0.0002460211,0.00006889232,0.0002235216],"domain_scores_gemma":[0.9992843,0.00007170124,0.0001067353,0.0003803307,0.00004366523,0.0001132425],"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":[0.00003735242,0.0003386837,0.07096164,0.00002400434,0.0009953283,0.00002499038,0.003841277,0.1604493,0.002085852,0.7600703,0.0001685296,0.001002705],"study_design_scores_gemma":[0.001291406,0.00007530442,0.2792669,0.00006725536,0.001462844,0.000003094721,0.001330417,0.6370105,0.004603422,0.07363838,0.0004141938,0.0008362301],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5087901,0.000009335681,0.489912,0.00002691122,0.00007801962,0.0000241779,0.000002323754,0.00004095577,0.001116148],"genre_scores_gemma":[0.9985515,0.00001399577,0.001031676,0.000116759,0.00001490986,1.140782e-7,0.000005374464,0.000003970719,0.00026174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6864319,"threshold_uncertainty_score":0.3782296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07397430108549308,"score_gpt":0.1993821756426765,"score_spread":0.1254078745571834,"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."}}