{"id":"W2923289556","doi":"","title":"A new dog learns old tricks: RL finds classic optimization algorithms","year":2019,"lang":"en","type":"article","venue":"International Conference on Learning Representations","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Optimization algorithm; Algorithm; Mathematical optimization; 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":[0.002395501,0.001207264,0.001623363,0.0008858475,0.0005813155,0.001734181,0.002074725,0.003611879,0.006717972],"category_scores_gemma":[0.01000278,0.0008027235,0.0007612329,0.0006584379,0.002434701,0.004829925,0.002450304,0.002687282,0.001383331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007839406,"about_ca_system_score_gemma":0.001002943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001518338,"about_ca_topic_score_gemma":0.001672309,"domain_scores_codex":[0.9993144,0.0002302388,0.00004186707,0.0002089211,0.0001412006,0.00006338714],"domain_scores_gemma":[0.9974505,0.00144611,0.0001522477,0.0005708977,0.0002581336,0.0001220975],"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":[0.0004587637,0.0002823312,0.002578764,0.0003982921,0.000281656,0.0001850816,0.0002736909,0.4709583,0.004040535,0.1102596,0.01428963,0.3959934],"study_design_scores_gemma":[0.00006663352,0.00009826341,0.0001572629,0.00003513219,0.00003245394,0.00008162545,0.00002810819,0.9397292,0.0008650038,0.0564019,0.002489236,0.00001526575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04851241,0.001424623,0.9331961,0.002159616,0.0004863029,0.00008325806,0.00008266522,0.001675654,0.01237946],"genre_scores_gemma":[0.662826,0.0006090869,0.3154438,0.00109212,0.000483449,0.0001880552,0.0001641205,0.0005803679,0.01861309],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006717972,"threshold_uncertainty_score":0.02247387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04493808541385731,"score_gpt":0.3381104561314346,"score_spread":0.2931723707175773,"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."}}