{"id":"W3212640884","doi":"","title":"Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis","year":2021,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Generalization; Computer science; Benchmark (surveying); Artificial intelligence; Meta learning (computer science); Property (philosophy); Probably approximately correct learning; Norm (philosophy); Upper and lower bounds; Statistical learning theory; Theoretical computer science; Algorithm; Machine learning; Generalization error; Mathematics; Unsupervised learning; Task (project management)","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":[0.01716828,0.002754235,0.003140899,0.003088226,0.001779738,0.00453835,0.006438899,0.004748514,0.006221009],"category_scores_gemma":[0.09142778,0.001405716,0.002712401,0.003254053,0.006347851,0.016399,0.009829555,0.01275414,0.001358625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004891871,"about_ca_system_score_gemma":0.001949958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001430375,"about_ca_topic_score_gemma":0.001385053,"domain_scores_codex":[0.9921114,0.002961684,0.0003752926,0.00163532,0.002317416,0.0005989068],"domain_scores_gemma":[0.9388589,0.04702408,0.002792702,0.007018859,0.003094761,0.001210789],"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":[0.0002118085,0.0001515066,0.001461097,0.0005716027,0.0002732748,0.000187428,0.0003455426,0.3004134,0.002453354,0.6369734,0.006706684,0.05025085],"study_design_scores_gemma":[0.00001266899,0.00008069666,0.0002808752,0.0001100639,0.00003806024,0.00009892876,0.00003025317,0.5534508,0.0008464171,0.4436294,0.001387772,0.00003393018],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009549852,0.002209392,0.9791825,0.002199063,0.0001333257,0.00008925638,0.0002301855,0.0003427772,0.006063642],"genre_scores_gemma":[0.6981818,0.005682026,0.2794674,0.00415417,0.001738297,0.001266166,0.001004554,0.001092227,0.007413319],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01716828,"threshold_uncertainty_score":0.09079564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07340860932113553,"score_gpt":0.198234698922775,"score_spread":0.1248260896016395,"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."}}