{"id":"W3159134962","doi":"","title":"Does Invariant Risk Minimization Capture Invariance","year":2021,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Invariant (physics); Minification; Generalization; Computer science; Population; Applied mathematics; Mathematical optimization; Mathematics; Artificial intelligence; Mathematical analysis","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":[0.006193765,0.0009773239,0.001822741,0.000792436,0.0005264512,0.002843505,0.001705827,0.002151764,0.003865904],"category_scores_gemma":[0.0412639,0.0006552978,0.001205931,0.0005677934,0.003509525,0.006580564,0.003337941,0.003497261,0.001128975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001022432,"about_ca_system_score_gemma":0.001269941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003077638,"about_ca_topic_score_gemma":0.001757566,"domain_scores_codex":[0.9958284,0.001576955,0.0001818839,0.001369418,0.0005711739,0.0004721855],"domain_scores_gemma":[0.9833786,0.009806916,0.00237528,0.003009974,0.0008925317,0.000536697],"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":[0.0001471413,0.0001347011,0.008615974,0.0003023557,0.0003324424,0.0002436915,0.0004502606,0.1921377,0.002287753,0.6855067,0.008197002,0.1016443],"study_design_scores_gemma":[0.00002444287,0.00007985196,0.002208534,0.00006277852,0.00003386366,0.0001050109,0.00008683386,0.350998,0.000594232,0.6426109,0.003159299,0.000036273],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06557621,0.0008797791,0.9132244,0.005442001,0.0001917894,0.00004161434,0.0002832445,0.0003767914,0.01398421],"genre_scores_gemma":[0.9076439,0.0009020704,0.08325652,0.00202354,0.0004915785,0.0001072588,0.0005140982,0.0005355141,0.00452554],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006193765,"threshold_uncertainty_score":0.03275621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0740804629402809,"score_gpt":0.3117591392564938,"score_spread":0.2376786763162129,"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."}}