{"id":"W4398160794","doi":"10.1609/aaaiss.v3i1.31228","title":"Federated Variational Inference: Towards Improved Personalization and Generalization","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Symposium Series","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute","funders":"","keywords":"Generalization; Inference; Computer science; Generative model; Machine learning; Personalization; Generative grammar; Bayesian inference; Artificial intelligence; Approximate inference; Process (computing); Stateless protocol; Bayesian probability; Bayes' theorem; State (computer science); Algorithm; Mathematics","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":[0.007669528,0.00156826,0.003059177,0.001082333,0.001074607,0.002366091,0.005188853,0.002839946,0.002676813],"category_scores_gemma":[0.02098079,0.001478159,0.001918983,0.001708502,0.002545938,0.006880711,0.004916449,0.005164717,0.000750664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002826149,"about_ca_system_score_gemma":0.002624439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008853287,"about_ca_topic_score_gemma":0.009751112,"domain_scores_codex":[0.996494,0.001292442,0.0001649185,0.0009823984,0.0007172812,0.0003490504],"domain_scores_gemma":[0.9898436,0.005212369,0.0004804337,0.0034131,0.0007706207,0.0002797668],"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":[0.0002095759,0.0001567625,0.001787381,0.00006140074,0.0001370734,0.0001151901,0.0002130438,0.8785477,0.001426767,0.0440517,0.003574186,0.06971917],"study_design_scores_gemma":[0.000007249212,0.000008270889,0.00004996755,0.000003729106,0.000005321998,0.00001530008,0.000006141212,0.9786159,0.0002548292,0.02081368,0.0002152167,0.000004473092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01424191,0.0002675957,0.982851,0.00040018,0.00003021514,0.00005005216,0.0001333641,0.001038287,0.0009874009],"genre_scores_gemma":[0.6955277,0.0004417866,0.2964838,0.0007785551,0.000158146,0.0002397679,0.0007736247,0.0005937489,0.00500287],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008853287,"threshold_uncertainty_score":0.04056078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01353260216856566,"score_gpt":0.2459461512685474,"score_spread":0.2324135490999818,"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."}}