{"id":"W4282966575","doi":"10.1109/icpr56361.2022.9956256","title":"Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch","year":2022,"lang":"en","type":"article","venue":"2022 26th International Conference on Pattern Recognition (ICPR)","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Alberta","funders":"","keywords":"Reinforcement learning; Computer science; Combinatorial optimization; Artificial intelligence; Heuristics; Inference; Machine learning; Monte Carlo tree search; Heuristic; Optimization problem; Mathematical optimization; Monte Carlo method; 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":[0.002938318,0.001660546,0.002430863,0.0009917071,0.0006863915,0.001596506,0.001916375,0.001839377,0.005549501],"category_scores_gemma":[0.00995898,0.0008526702,0.0007967375,0.001190958,0.001985643,0.002289307,0.00180748,0.00377397,0.0009242839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002069017,"about_ca_system_score_gemma":0.00317374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005109679,"about_ca_topic_score_gemma":0.005816454,"domain_scores_codex":[0.9990658,0.0004115245,0.00004790434,0.0001573486,0.0002007849,0.0001166462],"domain_scores_gemma":[0.9946166,0.004274497,0.0002616839,0.0003603301,0.0003185445,0.0001684031],"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":[0.00008002121,0.00008836136,0.0004896072,0.00009306397,0.00004000566,0.00003050069,0.00003029337,0.8919235,0.0004335184,0.03140542,0.002113969,0.07327165],"study_design_scores_gemma":[0.000007444161,0.00000912624,0.00001598307,0.00000599396,0.00000240414,0.00000314407,0.000001877338,0.991104,0.0001040344,0.008563235,0.0001811363,0.000001732354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007241201,0.0004472559,0.9886889,0.0002742084,0.00005040778,0.00004348245,0.0000312269,0.0005841673,0.00263917],"genre_scores_gemma":[0.460742,0.000700738,0.5323185,0.0005866153,0.000177307,0.0005406811,0.0002694122,0.0004960675,0.004168738],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005549501,"threshold_uncertainty_score":0.01856488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1202884624938102,"score_gpt":0.3655548205745376,"score_spread":0.2452663580807274,"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."}}