{"id":"W3174799275","doi":"","title":"Learning and testing junta distributions with sub cube conditioning","year":2021,"lang":"en","type":"article","venue":"Conference on Learning Theory","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Conditioning; Combinatorics; Distribution (mathematics); Mathematics; Logarithm; Bhattacharyya distance; Probability distribution; Discrete mathematics; Algorithm; Computer science; Statistics; 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":[0.007194861,0.002105433,0.002689458,0.001464314,0.001577578,0.003287988,0.005979499,0.002983042,0.00777153],"category_scores_gemma":[0.07238828,0.001030519,0.002795173,0.002341473,0.003438934,0.009807803,0.006473823,0.006532435,0.00211495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00206623,"about_ca_system_score_gemma":0.002812539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003965217,"about_ca_topic_score_gemma":0.0047672,"domain_scores_codex":[0.991583,0.002821504,0.0005723474,0.002502666,0.001892223,0.0006281614],"domain_scores_gemma":[0.9298368,0.05325544,0.002523942,0.00973039,0.003011523,0.001641891],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00450004,0.001609259,0.05058598,0.000638981,0.0005961251,0.0008212796,0.0008896531,0.4183912,0.007445537,0.1925536,0.02697094,0.2949974],"study_design_scores_gemma":[0.0001024661,0.0001983339,0.001076902,0.00002848478,0.00002640755,0.0001716869,0.0001206942,0.8576603,0.002575392,0.1369483,0.001062283,0.00002872881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.230215,0.0007719811,0.7532955,0.002881724,0.0002157006,0.0003835358,0.001718258,0.005577236,0.004941028],"genre_scores_gemma":[0.7244607,0.000329905,0.2609221,0.001653109,0.0004299077,0.0007161081,0.006411941,0.0009361784,0.004139928],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00777153,"threshold_uncertainty_score":0.03805053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01607629887651031,"score_gpt":0.2527456540907559,"score_spread":0.2366693552142456,"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."}}