{"id":"W4409365436","doi":"10.1609/aaai.v39i12.33362","title":"Hyperparametric Robust and Dynamic Influence Maximization","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Supercomputing Centre Singapore; National Research Foundation","keywords":"Maximization; Computer science; Mathematical optimization; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005056877,0.0001920332,0.0002967859,0.0002013757,0.0001601865,0.0001243003,0.0005311593,0.0001039627,0.00006086642],"category_scores_gemma":[0.007812046,0.0001406035,0.00005428409,0.0009792882,0.0003974328,0.0001201905,0.0001983954,0.0002660284,0.00001124458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004062888,"about_ca_system_score_gemma":0.00006634936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002412801,"about_ca_topic_score_gemma":0.000004901428,"domain_scores_codex":[0.9985644,0.00002584634,0.0005378617,0.0003553528,0.0002777964,0.0002387283],"domain_scores_gemma":[0.9981098,0.0007373847,0.000265408,0.0001927939,0.0006364761,0.00005818759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004189003,0.00007601405,0.0004549569,0.0001224781,0.000011745,1.207682e-7,0.0001352462,0.00004270101,0.005638293,0.9223028,0.0000467583,0.07112701],"study_design_scores_gemma":[0.00002545534,0.00008091582,0.00187555,0.0003628114,0.0000377545,0.000001737901,0.0002808117,0.05156988,0.04835967,0.8972481,0.00001651404,0.0001408055],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7855025,0.00005780428,0.1906105,0.002124079,0.0003020541,0.0007123824,0.00001593604,0.00009448679,0.02058026],"genre_scores_gemma":[0.9436509,0.00007318104,0.05573081,0.0001552697,0.000009279621,0.00002363751,2.205009e-7,0.00001016776,0.0003465108],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1581485,"threshold_uncertainty_score":0.9352313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.112658887432473,"score_gpt":0.3678255125654279,"score_spread":0.255166625132955,"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."}}