{"id":"W2096352571","doi":"10.1109/tsp.2004.831128","title":"Efficient Adaptive Algorithms and Minimax Bounds for Zero-Delay Lossy Source Coding","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Mathematics; Minimax; Bounded function; Upper and lower bounds; Algorithm; Redundancy (engineering); Rate–distortion theory; Discrete mathematics; Distortion (music); Combinatorics; Data compression; Computer science; Mathematical optimization; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002395124,0.0002381686,0.0002149803,0.0001749906,0.001005958,0.0004053262,0.0003561498,0.00009906096,0.000004458989],"category_scores_gemma":[0.000002496806,0.0002151949,0.00008564706,0.000344589,0.0001138395,0.0004633775,0.000008928429,0.0002421957,0.000007538505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001029855,"about_ca_system_score_gemma":0.0001606323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002311224,"about_ca_topic_score_gemma":0.000002297325,"domain_scores_codex":[0.9983568,0.00002570031,0.0002788141,0.0006071028,0.0003465422,0.0003850997],"domain_scores_gemma":[0.9992383,0.0001179455,0.0001150209,0.0002296775,0.000138304,0.0001607326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009097062,0.0002979949,5.169418e-7,0.00006637411,0.00002737765,0.00001231141,0.00175655,0.3269139,0.003536488,0.0005391014,0.00003525267,0.6667232],"study_design_scores_gemma":[0.001113743,0.0003438825,0.000005856682,0.0002616011,0.00002625668,0.00006756638,0.0001268248,0.9770308,0.01877024,0.00146439,0.0004800906,0.0003087944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00414165,0.0002454685,0.9946473,0.0001669094,0.0002087618,0.0002746108,0.0000244656,0.0001968972,0.00009390932],"genre_scores_gemma":[0.8291619,0.000005692985,0.1704109,0.0001934786,0.0000586962,0.00004663155,0.000001593932,0.00002080085,0.0001002684],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8250203,"threshold_uncertainty_score":0.8775392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02477583337455361,"score_gpt":0.2605651538793277,"score_spread":0.2357893205047741,"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."}}