{"id":"W3037881973","doi":"","title":"RelatIF: Identifying Explanatory Training Samples via Relative Influence","year":2020,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Outlier; Computer science; Machine learning; Constraint (computer-aided design); Class (philosophy); Training (meteorology); Artificial intelligence; Econometrics; Mathematics; Geography","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":[0.009727998,0.003038731,0.003450051,0.006383141,0.001500124,0.003215855,0.004073816,0.004228053,0.005398038],"category_scores_gemma":[0.04886378,0.0008942815,0.002431291,0.002623118,0.002261726,0.003658523,0.003038837,0.003528426,0.001839221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001456588,"about_ca_system_score_gemma":0.002214762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003551237,"about_ca_topic_score_gemma":0.004229085,"domain_scores_codex":[0.9939878,0.002758891,0.0003075293,0.001094659,0.001463256,0.0003878393],"domain_scores_gemma":[0.9756919,0.01962775,0.00104797,0.001558383,0.00158063,0.0004933291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001464132,0.0007894784,0.02565873,0.00108511,0.0006318701,0.0009765691,0.0009473017,0.3709819,0.00606559,0.02418375,0.01467905,0.5525365],"study_design_scores_gemma":[0.00009523045,0.0002339018,0.001571564,0.000127822,0.00008961555,0.0001981647,0.0001397843,0.9698703,0.003292668,0.02151995,0.002816926,0.00004408599],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06383695,0.002020272,0.9243937,0.0009422589,0.000114265,0.0006174627,0.0005375333,0.003363076,0.004174543],"genre_scores_gemma":[0.6036587,0.000614399,0.3869226,0.0007765336,0.0003721791,0.000881794,0.002728493,0.0007970965,0.003248141],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009727998,"threshold_uncertainty_score":0.05144721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2680933203368275,"score_gpt":0.3703719702066749,"score_spread":0.1022786498698474,"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."}}