{"id":"W4415427665","doi":"10.3233/faia251421","title":"Learning an Efficient Optimizer via Hybrid-Policy Sub-Trajectory Balance","year":2025,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; China Scholarship Council; National Natural Science Foundation of China; Danmarks Grundforskningsfond","keywords":"Flexibility (engineering); Inefficiency; Inference; Artificial neural network; Limiting; Domain (mathematical analysis); Optimization problem","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":[0.001213007,0.001248717,0.001095789,0.0004194463,0.0003856432,0.001227794,0.001528905,0.001354416,0.003660379],"category_scores_gemma":[0.003502557,0.0007658511,0.0006051683,0.0005236985,0.0009327492,0.001906857,0.001724077,0.002152297,0.001065306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001040119,"about_ca_system_score_gemma":0.001214835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00373561,"about_ca_topic_score_gemma":0.00374025,"domain_scores_codex":[0.9995655,0.0001082616,0.00002477243,0.0001426577,0.00009819892,0.00006052849],"domain_scores_gemma":[0.9991477,0.0005126718,0.00007087341,0.0001165128,0.0001040433,0.00004818769],"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":[0.00008364567,0.00007223482,0.0005484448,0.00006645172,0.00004625163,0.00006995745,0.00007530807,0.855258,0.003502271,0.01887068,0.002208265,0.1191983],"study_design_scores_gemma":[0.000005475766,0.00001200817,0.00002939461,0.000003246079,0.000003074352,0.000006627143,0.000003144876,0.9948357,0.0005124076,0.004313868,0.0002725773,0.000002403899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0112325,0.0002286015,0.9848905,0.0001806811,0.0000333414,0.00004234951,0.00003837163,0.0009081914,0.002445468],"genre_scores_gemma":[0.6070169,0.0004076218,0.3811827,0.0004841196,0.000106836,0.0003459517,0.0003551076,0.0007156431,0.009385101],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00373561,"threshold_uncertainty_score":0.01224518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01453050269501096,"score_gpt":0.2480937704820477,"score_spread":0.2335632677870368,"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."}}