{"id":"W4366197471","doi":"10.48550/arxiv.2304.06879","title":"Performative Prediction with Neural Networks","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Samsung; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Lipschitz continuity; Resampling; Convexity; Computer science; Artificial neural network; Performative utterance; Minification; Focus (optics); Mathematics; Applied mathematics; Mathematical optimization; Artificial intelligence; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003470398,0.0003171634,0.0002681117,0.000279912,0.0003560426,0.0002445044,0.001681596,0.0002514925,0.00001668074],"category_scores_gemma":[0.00003028225,0.0003251086,0.0001246718,0.001059308,0.0001157858,0.00109492,0.00164026,0.001016939,0.0001875823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002446561,"about_ca_system_score_gemma":0.0001229796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002816902,"about_ca_topic_score_gemma":0.0001386184,"domain_scores_codex":[0.9979764,0.0001812691,0.0002177517,0.001006264,0.0001257573,0.0004926053],"domain_scores_gemma":[0.9980667,0.0001520984,0.0002374599,0.001125155,0.0002486304,0.0001700182],"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":[0.00003136748,0.00003071485,0.002213159,0.00002533999,0.00005458037,0.0002822869,0.0006693762,0.9550089,0.000002872975,0.04059524,0.0003478275,0.0007382903],"study_design_scores_gemma":[0.0001077047,0.000137058,0.001270721,0.00007304276,0.00003409794,0.000007020641,0.0004277814,0.9869009,0.0001402952,0.01050559,0.00007527258,0.0003204742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1511078,0.00001577199,0.8455569,0.0001536579,0.0008962085,0.0003267903,0.000007668326,0.0007633517,0.001171837],"genre_scores_gemma":[0.9976272,0.00008363633,0.000703818,0.00007357523,0.0001346493,0.000003162558,0.00001926133,0.00002545384,0.001329217],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8465194,"threshold_uncertainty_score":0.9999201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1024308090817286,"score_gpt":0.1902680918126301,"score_spread":0.08783728273090152,"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."}}