{"id":"W3212595576","doi":"","title":"Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization","year":2021,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Oracle; Softmax function; Mathematics; Reduction (mathematics); Computer science; Context (archaeology); Mathematical optimization; Convex optimization; Algorithm; Regular polygon; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002405904,0.000171799,0.0002306178,0.00012457,0.0001725936,0.0004600282,0.0001122515,0.0001043254,0.000003627407],"category_scores_gemma":[0.0000840663,0.0001724722,0.00005884088,0.0002968714,0.00001220386,0.00148404,0.00002174834,0.0001151881,0.000003920047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008370138,"about_ca_system_score_gemma":0.00006073384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007124093,"about_ca_topic_score_gemma":3.697965e-7,"domain_scores_codex":[0.9989379,0.00005985341,0.0005309797,0.0001149406,0.0001318375,0.0002244784],"domain_scores_gemma":[0.9990783,0.00006905096,0.0001645854,0.0001846847,0.0004486115,0.00005477212],"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":[0.000008546453,0.00001429361,0.000009253948,0.0006087227,0.00003019447,0.000001362451,0.000984034,0.8517348,0.01047251,0.0003406078,0.0009929746,0.1348027],"study_design_scores_gemma":[0.0002077024,0.00002280835,0.000005261617,0.0001836081,0.00002117514,0.00006021481,0.0003180077,0.944812,0.0432035,0.00006027479,0.01090795,0.0001974909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003823019,0.0008127297,0.9905883,0.00005354659,0.0009400973,0.0003435202,0.000004784874,0.001735516,0.001698462],"genre_scores_gemma":[0.7118208,0.00003761442,0.2875796,0.0001299314,0.0001421906,0.0001071699,0.0001085787,0.00003342686,0.00004062866],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7079978,"threshold_uncertainty_score":0.703321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02658462693752342,"score_gpt":0.3057384159511689,"score_spread":0.2791537890136454,"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."}}