{"id":"W4399759465","doi":"10.48550/arxiv.2406.10161","title":"On the Computability of Robust PAC Learning","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computability; Computer science; Artificial intelligence; Mathematics education; Mathematics; Theoretical computer science","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":[0.006349665,0.001148834,0.001501362,0.001537621,0.001285417,0.004517555,0.002299905,0.002253953,0.005932411],"category_scores_gemma":[0.04096276,0.000682933,0.002102539,0.001271502,0.01009915,0.01197642,0.00605905,0.008549252,0.0007576358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003388724,"about_ca_system_score_gemma":0.001713336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001103113,"about_ca_topic_score_gemma":0.0007215958,"domain_scores_codex":[0.9936342,0.002188124,0.0003324306,0.001468428,0.001771391,0.0006053945],"domain_scores_gemma":[0.9453014,0.04126798,0.002657315,0.007541804,0.002101472,0.001130172],"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":[0.00006163383,0.0000274941,0.0005661191,0.0001303358,0.0000336259,0.00006048808,0.0001257864,0.03585018,0.0009371067,0.9554324,0.0008643295,0.005910556],"study_design_scores_gemma":[0.00001091287,0.00003194497,0.0002023655,0.00003276883,0.00001283625,0.00004325808,0.00002237033,0.1117912,0.001104521,0.8852499,0.001480322,0.00001750747],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06018611,0.001290456,0.9047314,0.005653711,0.0001695543,0.0001257245,0.0006132285,0.0006308436,0.02659889],"genre_scores_gemma":[0.9103593,0.001134507,0.08088778,0.001096425,0.0005954011,0.0002857638,0.0006065109,0.0003572258,0.0046771],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006349665,"threshold_uncertainty_score":0.03358066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06114499898615442,"score_gpt":0.1842416270676044,"score_spread":0.12309662808145,"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."}}