{"id":"W4414165913","doi":"10.1109/tcns.2025.3608004","title":"One-Point Sampling for Distributed Bandit Convex Optimization With Time-Varying Constraints","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Control of Network Systems","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Regret; Sublinear function; Upper and lower bounds; Convex function; Constraint (computer-aided design); Convex optimization; Benchmark (surveying); Projection (relational algebra)","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":[0.002043708,0.0002463585,0.0008527067,0.0003841638,0.0004516183,0.0002209831,0.0004754935,0.0001628446,0.0001505204],"category_scores_gemma":[0.0002067004,0.0002036222,0.000202374,0.001249257,0.0003021648,0.0003069996,0.000002571804,0.0002684405,0.00002212572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001601422,"about_ca_system_score_gemma":0.0002474417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001842662,"about_ca_topic_score_gemma":0.00001127217,"domain_scores_codex":[0.9963983,0.0003044514,0.001120849,0.0006070613,0.001039317,0.0005300363],"domain_scores_gemma":[0.9918698,0.00581615,0.0004342594,0.0005861177,0.001159019,0.0001346462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009063807,0.00009851065,0.00002870019,0.00003747937,0.0002569436,0.000001649635,0.00002742015,0.9876174,0.0001709073,0.00009270055,0.0003564735,0.01040549],"study_design_scores_gemma":[0.004377356,0.0003282975,0.00004352075,0.0004546424,0.0001038343,0.000006738605,0.0001309054,0.992941,0.0004350958,0.0004421166,0.0005292601,0.0002072419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001238087,0.0002038722,0.9955105,0.0003096939,0.0009877228,0.001951417,0.0005766391,0.00008423556,0.0002521297],"genre_scores_gemma":[0.9894567,0.00001393701,0.009359595,0.00005790756,0.000159082,0.0003138745,0.00002333472,0.00002685595,0.0005887158],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9893329,"threshold_uncertainty_score":0.8303474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05865261333941649,"score_gpt":0.3494235832352155,"score_spread":0.290770969895799,"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."}}