{"id":"W3110181803","doi":"10.1609/aaai.v35i8.16820","title":"Decentralized Multi-Agent Linear Bandits with Safety Constraints","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Science Foundation","keywords":"Regret; Computer science; Mathematical optimization; Network topology; Upper and lower bounds; Bipartite graph; Linear programming; Graph; Telecommunications network; Mathematics; Theoretical computer science; Computer network; Machine learning","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":[0.001289645,0.0008075314,0.001001897,0.0003069279,0.0006229776,0.001091813,0.001214065,0.001100983,0.001974149],"category_scores_gemma":[0.005873162,0.0003716897,0.0005250713,0.000507453,0.001090849,0.001607585,0.001420925,0.00147598,0.0003268543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009507649,"about_ca_system_score_gemma":0.001181039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002443726,"about_ca_topic_score_gemma":0.0015869,"domain_scores_codex":[0.9989379,0.0004389432,0.00003164846,0.0002157399,0.0002050927,0.0001706405],"domain_scores_gemma":[0.9966627,0.001969242,0.0005645233,0.000330871,0.0002888703,0.0001838255],"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":[0.00009740643,0.00004416767,0.000397107,0.00005743265,0.00002544301,0.00006151179,0.00004091539,0.9526244,0.001320817,0.03659026,0.0004893997,0.008251049],"study_design_scores_gemma":[0.00001267006,0.0000283412,0.00005514326,0.000003281514,0.00000357769,0.00001079264,0.00000946443,0.9875501,0.0002979271,0.01165652,0.0003685873,0.00000371089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04880285,0.0002230611,0.9466754,0.0003943587,0.00004933104,0.00005496777,0.00005903308,0.0001564732,0.003584552],"genre_scores_gemma":[0.9198722,0.0002428046,0.07560096,0.0001444319,0.00008717416,0.0001429728,0.0000969914,0.00005767875,0.003754807],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002443726,"threshold_uncertainty_score":0.006898344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2668979505252815,"score_gpt":0.4337449198338272,"score_spread":0.1668469693085457,"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."}}