{"id":"W3036904899","doi":"10.48550/arxiv.2006.13057","title":"PAC-Bayes Analysis Beyond the Usual Bounds","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Army Research Laboratory; Army Research Office; Engineering and Physical Sciences Research Council; University College London; DeepMind; Alberta Machine Intelligence Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Prior probability; Bayes' theorem; Bounded function; Generalization; Moment (physics); Computer science; Mathematics; Upper and lower bounds; Applied mathematics; Algorithm; Artificial intelligence; Bayesian probability","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01647729,0.003245312,0.003236509,0.002346121,0.001777455,0.005202306,0.003881249,0.003037883,0.00831241],"category_scores_gemma":[0.09169758,0.001538021,0.002286006,0.002340255,0.005628399,0.01510334,0.005605251,0.01347208,0.001829617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004952019,"about_ca_system_score_gemma":0.003694186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003705305,"about_ca_topic_score_gemma":0.002636209,"domain_scores_codex":[0.9875106,0.004364757,0.0004659496,0.002094048,0.004630587,0.0009341481],"domain_scores_gemma":[0.9232004,0.06309705,0.001873428,0.005372991,0.005530632,0.0009253916],"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":[0.00007381498,0.00005044601,0.0004206868,0.0002011531,0.00005731861,0.0001084113,0.0001722748,0.07101118,0.0004700195,0.8992118,0.005249736,0.02297312],"study_design_scores_gemma":[0.00001002114,0.00001791757,0.0001117459,0.00007799219,0.00001776251,0.00006219681,0.0000191588,0.2731853,0.0004878549,0.7225672,0.003424662,0.0000182618],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004021515,0.002842679,0.9704043,0.002714012,0.0002303948,0.00005855956,0.0002044711,0.0002564291,0.01926757],"genre_scores_gemma":[0.5179212,0.008844119,0.435804,0.00560678,0.003245553,0.001019686,0.0009052293,0.001249312,0.0254041],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01647729,"threshold_uncertainty_score":0.08714128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04837899876211513,"score_gpt":0.1949291850842765,"score_spread":0.1465501863221614,"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."}}