{"id":"W4386838733","doi":"10.1007/s10994-023-06391-0","title":"A general framework for the practical disintegration of PAC-Bayesian bounds","year":2023,"lang":"en","type":"article","venue":"Machine Learning","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Agence Nationale de la Recherche; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Generalization; Computer science; Bayesian probability; Artificial neural network; Artificial intelligence; Generalization error; Bayesian network; Machine learning; Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001007467,0.0001362143,0.0001696906,0.0001037861,0.0004496594,0.0001990566,0.0004317302,0.00007248657,0.00002431538],"category_scores_gemma":[0.001682953,0.00009042491,0.0001243474,0.0006522961,0.00004904868,0.0001728023,0.0001620762,0.0005748645,0.00002172855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001606617,"about_ca_system_score_gemma":0.0000517302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001514714,"about_ca_topic_score_gemma":0.00001366638,"domain_scores_codex":[0.9986961,0.0001947221,0.0002313796,0.0002890407,0.0002882875,0.0003004801],"domain_scores_gemma":[0.9979653,0.001393159,0.000164839,0.0003582306,0.00006238577,0.00005611854],"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":[0.0000376471,0.00006900111,0.01318367,0.00005744406,0.000085302,0.00001543614,0.003884296,0.04050262,0.0004517306,0.6136681,0.00184963,0.3261951],"study_design_scores_gemma":[0.0001662946,0.0001466311,0.003315957,0.00002195756,0.00001160735,0.0000113635,0.00007657203,0.958629,0.00009878999,0.01450261,0.02290826,0.0001109513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005282594,0.00008473445,0.9792858,0.01418169,0.0003855816,0.0001573223,0.000002441657,0.0003288664,0.000291003],"genre_scores_gemma":[0.7476931,0.00002336079,0.2493901,0.000239605,0.0004022716,0.0000292232,0.00002603063,0.00002491351,0.002171428],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9181264,"threshold_uncertainty_score":0.3687421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02232181877100274,"score_gpt":0.3316351353719595,"score_spread":0.3093133166009568,"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."}}