{"id":"W4415974865","doi":"10.1016/j.procs.2025.09.519","title":"Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression","year":2025,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Fondation du Risque; CNP Assurances","keywords":"Benchmark (surveying); Representation (politics); Focus (optics); Regression; Deep learning; External Data Representation; Latent variable; Big data","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":[0.004946521,0.0008347586,0.001170047,0.0008105356,0.0005671029,0.001231126,0.001970203,0.001180645,0.002853485],"category_scores_gemma":[0.01534527,0.0004223953,0.0008494726,0.0008885903,0.001106046,0.002383458,0.002226291,0.002654791,0.001010573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008581529,"about_ca_system_score_gemma":0.001227436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00225063,"about_ca_topic_score_gemma":0.003218004,"domain_scores_codex":[0.9985758,0.0006455936,0.00006720853,0.0002543038,0.0003430465,0.0001139072],"domain_scores_gemma":[0.9952388,0.00264978,0.0002817919,0.001069871,0.0005952186,0.0001645902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003895019,0.0002177424,0.004148314,0.0001562762,0.0001155914,0.0001833645,0.0001914349,0.65087,0.007209504,0.07150178,0.006421654,0.2585948],"study_design_scores_gemma":[0.000007662081,0.00002257438,0.0001593889,0.000007230297,0.00000361219,0.00001578115,0.000007841172,0.9847015,0.0007888297,0.01370314,0.0005780496,0.000004393267],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01460779,0.0002604088,0.983436,0.000156244,0.00004606221,0.00003812749,0.00007218407,0.000626212,0.0007568296],"genre_scores_gemma":[0.5678293,0.0003146591,0.4269961,0.0003507971,0.0001303793,0.0002984568,0.0008144175,0.0003719211,0.00289401],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004946521,"threshold_uncertainty_score":0.02616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01793323735750256,"score_gpt":0.308492226226615,"score_spread":0.2905589888691124,"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."}}