{"id":"W4406660008","doi":"10.1016/j.ipl.2025.106560","title":"Total variation distance for product distributions is <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si1.svg\"> <mml:mi mathvariant=\"normal\">#</mml:mi> <mml:mi mathvariant=\"sans-serif\">P</mml:mi> </mml:math> -complete","year":2025,"lang":"en","type":"article","venue":"Information Processing Letters","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Simons Institute for the Theory of Computing, University of California Berkeley; National Research Foundation Singapore; Science and Engineering Research Board; Amazon Web Services; National Science Foundation","keywords":"Product (mathematics); Variation (astronomy); Mathematics; Combinatorics; Discrete mathematics; Computer science; Physics; Geometry","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":[0.002552436,0.001095989,0.0009648096,0.003083903,0.0007720606,0.003093153,0.001849186,0.001022491,0.01971449],"category_scores_gemma":[0.01363049,0.0004429821,0.001199027,0.003171508,0.001682421,0.004258787,0.001962656,0.001609758,0.01073523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001288851,"about_ca_system_score_gemma":0.001398328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00271267,"about_ca_topic_score_gemma":0.00273512,"domain_scores_codex":[0.9970903,0.0006852851,0.0002136276,0.0007307746,0.001037022,0.0002429186],"domain_scores_gemma":[0.9933012,0.003110066,0.0003437984,0.001782743,0.001248583,0.000213602],"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":[0.0002178491,0.00009487551,0.001365512,0.0003593099,0.0001177569,0.0001846869,0.0002057648,0.02502505,0.003284723,0.6455089,0.04445839,0.2791771],"study_design_scores_gemma":[0.0000275195,0.0001450307,0.002831187,0.00008609696,0.00004002605,0.0008549268,0.0001164945,0.1784893,0.007234351,0.7460302,0.06405453,0.00009029736],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01114654,0.001077635,0.9633771,0.0007950715,0.0002262418,0.00005923222,0.002073091,0.001494743,0.01975039],"genre_scores_gemma":[0.4356906,0.005046061,0.4415173,0.001143613,0.000924464,0.0005465099,0.01819438,0.005037099,0.09190004],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01971449,"threshold_uncertainty_score":0.06595159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01297023851782032,"score_gpt":0.2337184539889568,"score_spread":0.2207482154711365,"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."}}