{"id":"W2998153559","doi":"10.48550/arxiv.1911.07357","title":"Random Restrictions of High-Dimensional Distributions and Uniformity Testing with Subcube Conditioning","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Microsoft Research; National Science Foundation","keywords":"Combinatorics; Conditioning; Bhattacharyya distance; Oracle; Mathematics; Multivariate random variable; Aggregate (composite); Key (lock); Component (thermodynamics); Distribution (mathematics); Computer science; Discrete mathematics; Random variable; Algorithm; Statistics; Artificial intelligence; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008624427,0.001473025,0.002764288,0.001512374,0.001326509,0.002224464,0.005203596,0.002327953,0.006140579],"category_scores_gemma":[0.0646134,0.0009837942,0.001952634,0.002341173,0.004601447,0.006329018,0.007248219,0.004829987,0.001288992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00188449,"about_ca_system_score_gemma":0.002593142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003536009,"about_ca_topic_score_gemma":0.002662386,"domain_scores_codex":[0.9899517,0.004807195,0.0005009634,0.002124886,0.00190194,0.0007132682],"domain_scores_gemma":[0.9474218,0.03872987,0.001946237,0.009079882,0.001820433,0.00100181],"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":[0.002189892,0.0005049702,0.01347089,0.0003265331,0.0003023131,0.0007376349,0.0006488359,0.3162393,0.009922437,0.4135389,0.01305243,0.229066],"study_design_scores_gemma":[0.0001113889,0.0001228554,0.0009470175,0.00002232817,0.00002767879,0.0001914143,0.00006250021,0.7117045,0.003442986,0.2821912,0.001135683,0.00004055525],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03338971,0.0001640657,0.9619904,0.0006685207,0.00004362018,0.0001429457,0.0003146261,0.00140026,0.00188586],"genre_scores_gemma":[0.4318922,0.0001524854,0.5616959,0.0009199099,0.0002672569,0.0007261391,0.001692147,0.0004752973,0.002178709],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008624427,"threshold_uncertainty_score":0.04561085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02084892164855933,"score_gpt":0.1637789221869524,"score_spread":0.1429300005383931,"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."}}