{"id":"W3042622665","doi":"10.4230/lipics.icalp.2022.71","title":"Downsampling for Testing and Learning in Product Distributions","year":2020,"lang":"en","type":"preprint","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Upsampling; Property testing; Monotonic function; Polynomial; Product (mathematics); Mathematics; Distribution (mathematics); Constant (computer programming); Regular polygon; Connection (principal bundle); Combinatorics; Discrete mathematics; Sample complexity; Time complexity; Function (biology); Sample (material); Algorithm; Computer science; Mathematical analysis; Artificial intelligence","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":[0.007657113,0.001773375,0.001935263,0.001603572,0.0008724773,0.00202021,0.003376534,0.001731113,0.004440742],"category_scores_gemma":[0.0380096,0.0009880926,0.002139747,0.001475494,0.003559283,0.008285429,0.004005162,0.005065198,0.0009703463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002435595,"about_ca_system_score_gemma":0.001802503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002333326,"about_ca_topic_score_gemma":0.001705116,"domain_scores_codex":[0.9945915,0.002020238,0.0002495286,0.00139395,0.001364301,0.0003803891],"domain_scores_gemma":[0.9697337,0.0230839,0.001278404,0.00404116,0.001259455,0.000603274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00120079,0.0004782373,0.006240268,0.0004802399,0.0002381984,0.0003686988,0.0004636667,0.3062649,0.007052343,0.3893744,0.009538823,0.2782994],"study_design_scores_gemma":[0.00004124418,0.00006165688,0.0003122597,0.00001540042,0.00001987458,0.00008853233,0.00001804088,0.868691,0.002349113,0.1275363,0.0008554122,0.00001119748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01789888,0.000315824,0.978297,0.0007312588,0.00004674389,0.00008447855,0.0001398849,0.000685959,0.001800027],"genre_scores_gemma":[0.3867923,0.0004199056,0.6069847,0.0006867874,0.0003141009,0.0004837709,0.0008440076,0.0003193885,0.003155064],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007657113,"threshold_uncertainty_score":0.04049516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03634888266614139,"score_gpt":0.2872981621214975,"score_spread":0.2509492794553561,"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."}}