{"id":"W7126437193","doi":"10.21428/594757db.be36222c","title":"Adaptive Learning Rates for Gradient Boosting Machines","year":2024,"lang":"en","type":"article","venue":"","topic":"Stochastic Gradient Optimization Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Boosting (machine learning); Gradient boosting; Hyperparameter; Rate of convergence; Convergence (economics); Online machine learning; Adaptive learning; Context (archaeology)","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":[0.008160103,0.001362848,0.001974717,0.001142539,0.0006910599,0.001783582,0.00224049,0.001853173,0.002580493],"category_scores_gemma":[0.0304469,0.0007191971,0.0009994131,0.001281852,0.001265413,0.002277867,0.001672609,0.003046628,0.002108414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00107375,"about_ca_system_score_gemma":0.001404656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001210915,"about_ca_topic_score_gemma":0.0009368351,"domain_scores_codex":[0.9966827,0.001779282,0.0001474944,0.0003663292,0.0008209797,0.0002032682],"domain_scores_gemma":[0.9944857,0.002839853,0.0004246812,0.000808533,0.001239812,0.0002015025],"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":[0.0002466787,0.0001218235,0.002123918,0.0002914238,0.0001290756,0.0001205384,0.0001712804,0.6686759,0.004290544,0.1358318,0.009392732,0.1786043],"study_design_scores_gemma":[0.0000245708,0.00003535466,0.0001350612,0.00002864239,0.00001231272,0.00004285677,0.000007856858,0.9621108,0.001119507,0.03331789,0.003152077,0.00001327093],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006019523,0.0007136454,0.9902569,0.0002800831,0.0001568968,0.00007757595,0.00003872646,0.0005491956,0.00190749],"genre_scores_gemma":[0.374496,0.001537679,0.6158322,0.0006036494,0.0004185266,0.0006675426,0.0003580874,0.0006185357,0.005467771],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008160103,"threshold_uncertainty_score":0.04315525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02590661414453695,"score_gpt":0.3005913982977793,"score_spread":0.2746847841532423,"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."}}