{"id":"W3162671465","doi":"10.1109/infocom42981.2021.9488698","title":"Delay-Tolerant Constrained OCO with Application to Network Resource Allocation","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada); Ontario Tech University; University of Toronto","funders":"","keywords":"Regret; Computer science; Mathematical optimization; Benchmark (surveying); Sequence (biology); Constraint (computer-aided design); Convex function; Convex optimization; Resource allocation; Sublinear function; Term (time); Regular polygon; Mathematics","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":[0.001348029,0.0001218688,0.0002062158,0.0001114609,0.0002022064,0.0002171047,0.0004566302,0.00005767483,0.0005667402],"category_scores_gemma":[0.0007598436,0.00008423271,0.00003965257,0.002143008,0.0001001316,0.0001890872,0.000145866,0.0001425774,0.0007448636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005891302,"about_ca_system_score_gemma":0.0002064284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001181608,"about_ca_topic_score_gemma":0.0002063825,"domain_scores_codex":[0.9968409,0.000161825,0.0004059999,0.0006613719,0.001552012,0.0003778787],"domain_scores_gemma":[0.9968142,0.001089602,0.0000890474,0.00081532,0.0009607536,0.000231076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001124507,0.00005508501,0.0005009446,0.00000205245,0.00001619492,0.00003862406,0.000218178,0.7180675,0.0009103372,0.002828788,0.009275641,0.2679742],"study_design_scores_gemma":[0.0007798178,0.0001815299,0.002228154,0.00002510312,0.000008681852,0.00008934754,0.001753539,0.3685828,0.003719368,0.01008731,0.6121755,0.0003688007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003849968,0.0000497392,0.9821724,0.003652558,0.00003908314,0.0004228621,0.000005612552,0.0000688871,0.009738905],"genre_scores_gemma":[0.8900028,0.000003221485,0.09547024,0.001543157,0.0002805279,0.0001440437,0.00003341202,0.00002256364,0.01250006],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8867021,"threshold_uncertainty_score":0.9573964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04998465779926876,"score_gpt":0.3846377056724134,"score_spread":0.3346530478731446,"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."}}