{"id":"W3022187609","doi":"10.1016/j.ipl.2020.105973","title":"Randomized distributed online algorithms against adaptive offline adversaries","year":2020,"lang":"en","type":"article","venue":"Information Processing Letters","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Danmarks Frie Forskningsfond","keywords":"Adversary; Competitive analysis; Adversary model; Online algorithm; Computer science; Randomized algorithm; Randomization; Algorithm; Deterministic algorithm; Theoretical computer science; Mathematics; Upper and lower bounds; Computer security; Randomized controlled trial","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":[0.008544007,0.002599576,0.003364652,0.001293684,0.001559412,0.003034162,0.00480356,0.00447648,0.005141825],"category_scores_gemma":[0.03521571,0.0013405,0.001152641,0.001786988,0.004256411,0.006179464,0.007237494,0.005523819,0.001319266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002750864,"about_ca_system_score_gemma":0.003488828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009248005,"about_ca_topic_score_gemma":0.001146262,"domain_scores_codex":[0.9916642,0.00365449,0.0003435314,0.001711674,0.001545531,0.001080554],"domain_scores_gemma":[0.9509995,0.03682192,0.002193287,0.007032354,0.001741025,0.001211864],"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":[0.002914752,0.0004942759,0.001077933,0.0002761302,0.0001796904,0.0001612816,0.0001218388,0.8456045,0.003935446,0.08725231,0.007612947,0.05036886],"study_design_scores_gemma":[0.0001670901,0.000112804,0.00009080009,0.00001568161,0.00001946152,0.00004582387,0.00001758026,0.958276,0.001233003,0.0395693,0.0004394008,0.00001302062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06052782,0.0008102083,0.922963,0.002866786,0.0004974395,0.0002809063,0.0002842961,0.001588878,0.01018068],"genre_scores_gemma":[0.8796347,0.0003392511,0.1093822,0.0007938961,0.0003842379,0.0005244802,0.0002509196,0.0003430194,0.008347459],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008544007,"threshold_uncertainty_score":0.04518563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02585856871424095,"score_gpt":0.2456301909840091,"score_spread":0.2197716222697681,"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."}}