{"id":"W4409363537","doi":"10.1609/aaai.v39i19.34197","title":"Federated Unsupervised Domain Generalization Using Global and Local Alignment of Gradients","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generalization; Domain (mathematical analysis); Computer science; Artificial intelligence; 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":[0.000343313,0.0001498681,0.0002032738,0.00009560749,0.0001975217,0.0001727884,0.0005799585,0.0000655613,0.0000124468],"category_scores_gemma":[0.0001039362,0.0001230212,0.00005346385,0.0008210749,0.0002477178,0.0002342662,0.0002515578,0.00009575178,0.000003032546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007557218,"about_ca_system_score_gemma":0.0001039967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007566408,"about_ca_topic_score_gemma":0.000009025757,"domain_scores_codex":[0.9986408,0.00003133281,0.0004621936,0.0003347056,0.0003280196,0.0002029944],"domain_scores_gemma":[0.9990982,0.00003055027,0.0002574867,0.0001194375,0.0004382913,0.00005604851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000421582,0.00007838175,0.001123296,0.00004049524,0.00001849753,2.06203e-7,0.0006567539,0.0006898791,0.0376768,0.9115468,0.00002012973,0.04810666],"study_design_scores_gemma":[0.00008632016,0.00009206207,0.0008958014,0.0002849109,0.00001398896,0.000002538304,0.001213275,0.643764,0.2054708,0.1479653,0.00005787244,0.0001532137],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3702855,0.00002906374,0.6252293,0.0007481,0.0002080473,0.0002175588,0.000002133569,0.00003124668,0.003249037],"genre_scores_gemma":[0.9917031,0.00001959901,0.007989855,0.0002111521,0.00000949565,0.000005062348,5.254187e-7,0.000004012019,0.00005722315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7635815,"threshold_uncertainty_score":0.5016658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05622118777486132,"score_gpt":0.3013968096862158,"score_spread":0.2451756219113545,"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."}}